And this + the tendency for AI to "prefer" AI produced code + some other AI biased is why *this is most likely highly illegal to use in the EU due to violating anti discrimination laws in multiple ways.
To be clear:
- randomly filtering "too many" resumes is pretty much allowed (I think)
- but must be actual random independent of the resume (and can be in multiple layers, i.e. random filter > pre-select > random filter > select)
- this isn't the case for AI as the random aspect isn't done as the random aspect is not independent of the actual resume evaluation
- in general you can't make sure the AI doesn't apply systematic biases, and there is high indication that it does do so
- for humans you can train them and order them to ignore their biases, this won't work reliable either _but now you delegated the responsibility of illegal biases to the hiring personal violating the order_. But for AI usage you are responsibility no matter what you tell it. Lastly you can technically "show/proof" a specific used AI is highly biased in a specific contexts, which for human employees is technical possible but practical not really practical. So this moves "specific mostly deniable" cases, into "systematic proven bias" teritory. Or in other word legal risk goes from "limited/no issue" to "people can systematically f-you over if they know you use AI for hiring".
An alarming number of people don't understand that LLMs work via purely stochastic processes, so I'm happy to see in-depth pieces like this. I'm looking for a job and maybe this is why it's so hard to get a callback these days: resumes are just dumped in some LLM black hole and no one really knows how it works. The author says:
> temperature 0.1 — low, supposedly nudging the model toward deterministic outputs
This is not correct (and is briefly touched on later in the piece when he sets temperature to 0), temperature is not some kind of "deterministic" switch, but rather it affects the sampling distribution (which becomes more "spiky"—but is still very much a distribution).
In theory, temperature 0 does make the LLM deterministic.
Well, in theory theory, temperature 0 doesn't really exist. Mathematically, as lim temperature->0, the distribution gets spikier and spikier, the most likely sample goes to almost-but-not-quite infinity and the rest go to almost-but-not-quite 0. In practice, temperature=0 is literally a separate branch of an if statement that just picks the most common sample (using the actual formula that works for non-zero values would cause a zero division).
However, due to things such as batching and even different kinds of floating point imprecisions for different algorithm implementations, the probability distribution itself often differs run-by-run, so what you sample from it also differs.
>in theory theory, temperature 0 doesn't really exist.
It does exist very much, even if you go to pure math. Look at the softmax function and take the limit as T->0. It becomes a dirac-delta function. I.e. in a discrete setting (like for LLMs with a finite set of output tokens), probability P becomes one for argmax and 0 for everything else. Only in coding practice it is easer to implement T=0 as a simple if check that directly chooses argmax instead of calculating the limit of some function that includes 1/T quotients. But setting T to zero is in both, theory and practice, turning the usual probability function into greedy sampling.
> Look at the softmax function and take the limit as T->0. It becomes a dirac-delta function.
In pure math, it does not always do that. It becomes a dirac-delta comb with equal weight on every maximum. There can be more than 1 maximum. Setting the temperature to zero turns into greedy sampling, but greedy sampling is not necessarily deterministic as you can have multiple equally optimal options.
That is not a problem for LLMs, because in practice floating point inaccuracies (in particular after exponentiation) prevent values from being exactly equal. That's why greedy sampling generally produces deterministic output for LLMs. The real gotchas are elsewhere (like with batch inference as we've seen with earlier GPTs). But unlike what the earlier comment says, this is a non-issue mathematically.
> That is not a problem for LLMs, because in practice floating point inaccuracies (in particular after exponentiation) prevent values from being exactly equal
Any two tokens ending up with the exact same logit is very unlikely, but not impossible; and as the number of output tokens grows, the odds that it will happen eventually gets higher and higher.
I suppose, to ensure determinism, rank by logit then token ID, so you still have a deterministic winner even if occasionally two tokens get precisely identical logits.
> It becomes a dirac-delta function. I.e. in a discrete setting (like for LLMs with a finite set of output tokens), probability P becomes one for argmax and 0 for everything else. Only in coding practice it is easer to implement T=0 as a simple if check that directly chooses argmax instead of calculating the limit of some function that includes 1/T quotients.
I don't understand the distinction you're drawing. A Dirac delta function is a "simple if check".
The point is that the case T=0 doesn't just "exist" as a special code branch - it is still well defined mathematically without any change to the output function. What the above comment refers to with the extra "if" check is just a limitation of computers not liking to divide anything by zero, even if the actual function exists and is well behaved at zero. It is not some weird or special theoretical construction.
> However, due to things such as batching and even different kinds of floating point imprecisions for different algorithm implementations, the probability distribution itself often differs run-by-run, so what you sample from it also differs.
Exactly. While I’m assuming this won’t be news for most here, for those that are still new and/or curious about some more explanation on e.g. the floating-point imprecisions, see this nice article: https://thinkingmachines.ai/blog/defeating-nondeterminism-in...
> Mathematically, as lim temperature->0, the distribution gets spikier and spikier, the most likely sample goes to almost-but-not-quite infinity and the rest go to almost-but-not-quite 0.
That's not how limits work. As the temperature goes to 0, the rest goes to 0. That's it. The "almost-but-not-quite" is part of the "goes to".
Let's say f(x) = 3x+1. It's a continuous function. If we let x go to 10, f(x) goes to 31. Not "almost-but-not-quite 31". No, to 31. (If you don't have a continuous function then it's the same argument, but less intuitive to illustrate.)
> However, due to things such as batching and even different kinds of floating point imprecisions for different algorithm implementations, the probability distribution itself often differs run-by-run
The implementation does not often differ run by run.
> The implementation does not often differ run by run.
If you use a cluster, or even multiple clusters, and they have non-identical hardware, then two consecutive runs could end up being routed to nodes having different GPU models with slightly different floating point behaviour, or even software differences (e.g. newer GPU offers some feature usable to speed up calculations which older model lacked; same code can use the feature when it is available, fall back to slower alternative if it isn’t). The larger your scale, the greater the odds it will happen
If you make an exact integer implementation and run with temp=0 it's deterministic.
You don't even need temperature 0, just make a random seed for the sampler part of the input and then its deterministic as a function of the input.
But running autoregressive models at temp=0 tends to expose pathological behavior, because the training process produces a function with a lot of gain so its prone to feedback on its own noise.
As I understood it, the "randomness" affecting what is selected at any temperature still comes from a PRNG or CSPRNG (or whatever RNG you want, maybe a hardware one), and if you where to swap out that with something deterministic you'd get the same results every time (barring non-determinism in other parts of the OS/drivers/maybe even hardware).
But theoretically, the output of every LLM is seed-driven (or could be if you wrote the software to isolate it) just like any computer software. It's just none of the software written (even llama.cpp AFAIK) chooses to support stable-seeding due to the changes in stuff like CPU/Vulkan/CUDA/Metal differences making it difficult to make consistent.
They could though! Hopefully one day someone implements it into the mainstream LLM-engine software and it gets exposed in the APIs serving the models. It'd do a lot to show folks the "internals" of these models.
Stable seeding is not enough. A lot of modern, fast compute kernels are nondeterministic. Floating point multiplication/addition is not strictly associative and e.g. reductions can combine results from different threads in different orders (e.g. through atomic ops). You can write kernels to be deterministic, but it is generally less efficient.
It's probably due to the fact that it's a cloud service. You have no guarantee that your next request will go to the same machine. So even with an identical seed, and temp 0 you might get different hardware and hence different accuracy/noise in the floating point operations.
How can there be noise in floating point operations? I could buy like completion order for parallized batches i.e. adding a+b+c is different from a+c+b etc.
While the IEEE 754 standard ensures that individual basic operations are deterministic and strictly bounded, it does not guarantee that an entire program will yield bit-identical results on all CPUs.
CPUs and their execution environments introduce subtle hardware variations, architecture choices, and compiler optimizations that break bit-level consistency.
To be clear, temperature 0 is deterministic and will produce the same output for exact duplicate inputs, across all seed choices.
Provided:
* If it’s MoE we are talking about, that the duplicate inputs are for the whole batch (yes, your batch neighbours can impact your choice of experts. Blergh.)
* Your kernels are deterministic
* There’s no system wide effort switch that responds to, e.g. work load across the cluster (for a thinking model)
Upshot:
Temperature 0 is not deterministic in probably any existing cloud infra, but it could be for edge inference pretty reliably.
To your quibble on 0.1 being more deterministic - I think it’s a pretty fair summary - we’re going to sample much more from the ‘temp 0’ answer at 0.1 than we would at temp 0.9, no?
Even then it's deterministic in the way a hash function is deterministic. Change one letter and you can get a completely different output. What people actually want is something continuous.
A distribution with all probability mass on one outcome is deterministic, so in principle, setting temperature to 0 _should_ result in deterministic outputs. There are a few reasons it might not, but I don't think any of these apply when running a local model like the author did.
> so in principle, setting temperature to 0 _should_ result in deterministic outputs
It is a common misconception, but it is not true even in principle. If I have 2 or more logits which are equal to the maximum of my logits, I will sample uniformly random from them with any temperature, even zero. Sampling from softmax([1, 0, 1]) is still stochastic at temperature 0, because the limit is to sample uniformly from the first or the last element.
Anyway: "GPUs don't do deterministic matrix multiplications" is the biggest source of randomness in LLMs. GPUs put the associativity of the sums in matrix multiplications in arbitrary order, and this has a huge impact on the logits coming out of the neural network.
You don't have to sample uniformly. You could take the lowest index of all maxima.
But yeah, the main source of randomness is non-deterministic matmul, and temperature does nothing with it
> "GPUs don't do deterministic matrix multiplications" is the biggest source of randomness in LLMs.
But this isn't a fundamental property of LLMs, it's just an implementation detail. It's pretty obvious that if you evaluate the matrix multiplications correctly and deterministically sample from the highest-probability outputs, you will have a deterministic LLM.
It may be an implementation detail, but in practice, if the only way to get a deterministic output is to run on the CPU, then it's not going to be usable.
The matrix multiplication is only deterministic for sparse-dense products under these settings:
> torch.bmm() when called on sparse-dense CUDA tensors
And it's not listed under the operations that raise an exception otherwise, so I'm not sure the docs promise that dense-dense matrix-matrix products are deterministic.
There are. If the kernels are nondeterministic (e.g. timing issues) there are minor changes between runs, on a single system, even with eager decode enabled (typically what temperature=0 achieves).
Setting the temperature to 0 should give deterministic results but that's not any better - it's just hiding the huge variance by only taking one sample.
I mean the easiest explanation would be that the model harness doesn't always take the most likely token but does top-k sampling or similar. temperatur just means that probabilities get more and more equalized, boosting the chance that an unlikely token gets picked. but even with temp 0 you could have 0.8 T1, 0.19 T2, ... and sometimes sample T2
No, this can't happen at temperature 0. The formula defining temperature-adjusted softmax isn't strictly defined at 0, but taking the limit (in the case where all logits are distinct) results in probability 1 being placed on the largest logit. Samplers will typically special case temperature 0 and pick the most likely token at each step.
This is a very authoritative answer that should be more nuanced and caveated as implementation-dependent. In some cases, repetition penalties take precedence over sampling; top_k and top_p can also be handled before or after the temperature step. In other cases, `0` is turned into like 1e-10 or some super tiny float value (which can drift if you do any arithmetic with it). Routing, quantization, etc. can also have an effect on sampling. And yes, in some cases, setting temperature to 0 can mean "pure greedy decoding" which makes the decoder about as deterministic as it can get.
A more spikey distribution exactly makes the distribution closer to deterministic. That's not the point though. Even in greedy (deterministic) decoding, it is still a black box though that reacts in ways ways that are unpredictable to the inputs. Switching one word around might lead to different scores for example.
Yeah, this is the forest that the people arguing about math trees are missing. It doesn't matter that the algorithm is deterministic if the algorithm passes the input through a cryptographic hash function to make a yes/no decision. The result may be perfectly reproducible and still non-sensical in its distribution with respect to its input domain.
> An alarming number of people don't understand that LLMs work via purely stochastic processes ...
I've been studying AI for 20 years. What really needs to be added to this statement is:
"An alarming number of people don't understand that LLMs work via purely stochastic processes - and so does human thinking. People do NOT arrive at the same conclusion if merely the weather's different. Worse: with human thinking not only do most people not think this is real, a subset of people will actively fight the idea. Of course, depending on the weather"
Every time people point out a limitation or constraint of LLMs, I see a comment that is to the effect of “but humans…”. I don’t understand why this comparison is relevant to this particular thread. Is it just an amusing similarity?
The same person is not going to give you three different answers within span of minutes. Especially when nothing fundamentally has changed. People might or might not update their views depending on their biases.
I'm pretty sure the personality tests are created specifically for the reason that a single person can have fundamentally (or conflicting) beliefs about himself in a matter of minutes. You can say "I am honest person" and the next minute you can say "I never lie" - and both cannot be true for an average person.
We expect computers to be consistent on the other hand. A calculator will always give you the same answer unless some chip gets struck by a particle. LLMs are on computers and should be fairly consistent too.
Yeah but daily tools have lots of complexity which appears as non determinism (if we are thinking only UX, not actual determinism). For example, try moving an image in the word doc. I have been using MS word my entire life it seems, still don't know what the rules are lol.
What's even worse, different humans have different weights.
If you train two different LLMs and replace what data they "see" in batch n, that doesn't affect the data they see in batch n+1, or any further batches. In LLMs, you can introduce "noise" into the training process, but that noise doesn't really compound.
Humans learn from experience, not from data, and their experiences at age n shape what experiences they seek (and hence train on) at age n+1. A small amount of "noise" injected into their "training", let's say hearing a group of friends discuss a movie while their identical tween goes to the bathroom, can compound into them watching that movie, which can compound into them forming an identity around that genre, and so on, until they're two completely different people, trained on completely different "data mixtures".
Ah cool. So there is data? How consistent are humans?
What I'd really love is an actual number for a "human hallucination rate". How often will a random human
1) claim something that is wrong
2) defend the wrong claim and/or logic even when the problem is pointed out to them
(and this of course outside of the usual topics. In politics? I don't care. In religion? Don't care (well, maybe a bit more than politics). Let's say in physics or popular logic or something like that)
There is evidence that children will oscillate between understanding and not understanding while learning topics. Philip Sadler at Harvard published about this but i can't find the paper im thinking of on his google scholar. too many papers!
but moreover, to verify a test item you need to make sure that peopel will select the same answers under teh same conditions at different times. people generally forget the specific questions they were asked if you ask them the same questions a month later so being able to get them to answer the same way each time is important. it is assumed the people have some static knowledge of a topic in this scenario.
If you want to consider a statistical examination of how people answer tests and how we assess knowledge and other things in people through surveying you can read about item response theory and rasch analysis.
how did they account for sampling bias? a judge might leave easier cases for after lunch. people with control over their schedules usually ease themselves back into it after breaks.
If RL was used to train the model, the model will have been trained on its own sequences. Those will have been generated with a temperature of 1.0. They must be, otherwise you would get a premature collapse or explosion of your entropy if the temperature was respectively lower or higher.
After that RL step, you want to stick to the RL distribution, and so keep a temperature of 1.0. Other temperatures will drive the model out-of-distribution.
That is why the sampling step for agents or thinking LLMs are usually kept at a temperature of 1.0.
It really depends on the application does it not? I'm not an LLM guy, but for creative tasks like storytelling wouldn't you want a higher temperature usually? Happy to gain insight from anyone with experience here :)
Heavily depends on the model architecture and the implementation though, I don't think you can say what values are better than others without first specifying those, otherwise it's straight up guessing, ironically.
Willing to be corrected but I believe this type of automated resume filtering is illegal. Not saying it never happens but my understanding is it is not typical.
I don't know for sure, but I would be surprised if it was illegal in my particular US state. You might be able to argue the AI has inherent biases that introduce illegal discrimination in the hiring process, but my understanding is winning I case like that would be very difficult, especially since most employers are very cagey about their hiring process and why they mades a decision.
In situations when you get hundreds of applications for one open position (real market now), whatever reduces your pool to the size a human can handle, works. You can preserve some diversity metrics in the process. This particular filtering is rather primitive, but LLM as a first filter can definitely do the job. You may burn less tokens than the hourly rate of your HR and it will be fairer than just dumping 50% of unread CVs in trash.
Great until someone realises you’ve filtered out minority groups from the application process (most developers are men so maybe the LLM decided they’re the best fit, but you’ll never know exactly why it screwed your over) and you suddenly have an expensive lawsuit
LLMs are DEI-aware, as over past few years, their vendors all had various high profile news stories with their models and their default biases, so it's more likely they'll heavily discriminate in favor of minority candidates, not against them. Still, in both cases it would indicate whoever is operating the system is doing a really, really lazy job. It's really not hard to test and supervise LLMs on tasks where they give you mere 2-10x leverage, and prompt adherence today is much better than it was 3 years ago.
this happened a decade ago when a US courted tried to make sentencing decisions via ML. it was easialy demonstrated that the training data was flawed because the justice system was flawed so the data it was trained on was weighted against minorities because it oversampled because you know, police routinely oversample and poverty for es oversampling
nonetheless, people will defend history as perfect and say those samples, like nepo babies, are "perfect".
Under GDPR, you have the right to request manual processing whenever personal data is processed automatically to make a decision about you that has "significant impact". Not being hired seems like it would qualify.
At one point in the past a major UK a medical school adopted random selection for qualified candidates (Barts and The London School of Medicine and Dentistry - part of Queen Mary University of London). The approach benefitted qualified students from less well-off backgrounds vs those who can afford to win at the ever more elaborate (manual at the time) hurdles of resume assessment criteria and effectively game the system. There was an orchestrated campaign against the lottery around "Why gamble with would-be doctors?". Random selection was quietly dropped.
A person's total luck is constant over a lifetime. The remaining half of the candidates already spent some of their luck in this selection, so they'll be on average less lucky than the discarded half.
No, luck would be some expression of the difference between the average and the individual outcomes - it only exists relative to a population at the point in time when it is measured.
everyone knows luck is tied to the wealth-gravity and increases as the inverse distance to the density of matter. hut because its relative, everyone thinks they have the same luck when not observing others.
Even assuming that was genuinely how luck works, the conclusion does not follow from the premise because it’s obvious not everyone “starts with” the same amount of luck to spend.
Or more to the point. There are generally far more qualified applicants than job roles. That is training and education greatly expanded over the last couple of decades to produce more and more job seekers, whilst job creation hasn't really kept pace.
May be LLM resume screening is a symptom of a bigger problem - with tens of candidates per vacancy employers can screen resume badly and even throw half of the resumes away and still hire someone qualified.
> I fail 65% of the time. Same exact resume, different luck.
As someone who’s run hiring pipelines for technical roles in the past few years, that’s actually a fantastic number. I objectively hate saying that, but it’s true.
35% chance of elevating a technical individual to the next stage with no effort? I’ve seen as many as 100+ applicants an hour even when including a domain specific screener question. That’s 35 “screened” applicants in an hour. Were valid candidates screened out? Yes. Does you still have a candidate pool 35x larger than you need? Unfortunately, also yes.
The volume of applicants is SO HIGH such that your chances of getting moved to the next stage are actually markedly worse if AI isn’t involved. If you didn’t apply immediately (using an AI bot) there’s 50+ people ahead of you, and an exhausted technical leader if they ever make it to your resume.
So the logical solution is for candidates to submit multiple applications with slight variations to their contact info, "John Schmidt", "John J. Schmidt", "John J. J. Schmidt", "John Jacob J. Schmidt", "J. J. Jingleheimer Schmidt", etc.
Is it? Or is it a 65% chance of a resume getting ignored before a single human sees it, reducing your pipeline's likelihood of catching qualified candidates by the same?
Gates that reduce resume flow-through are only useful if their reduction is correlated with quality. Otherwise they're just dragging out your hiring process or unnecessarily causing you to ultimately lower your hiring bars.
> Gates that reduce resume flow-through are only useful if their reduction is correlated with quality.
The volume is infeasible to review everyone for quality, even at an hour scale. The conclusion and solution is inevitable, though I wish it were different. 35% is actually really good if you’re not coming in through a referral.
The current reality is <1% and the person reviewing you is exhausted.
What a inhumane way of looking at this. Hiring is deeply flawed, you know it, and yet you keep job postings open for weeks/months in case "the one" magically appears on your doorstep instead of just interviewing 10-20 people and just pick one...
What's the alternative? Everyones up in arms, but I see ZERO viable alternatives proposed.
If you have 1000 applications for every job, and you know that a bunch of these applications are "a bad fit", to put it mildly, you have to filter. And you cannot realistically give every resume a good, human look. By the time HR would be done, the market has already moved on five times.
So, what is the real difference between being overlooked because HR could only look at the first 100 resumes, or the AI filtered all 1000 resumes down to 100? In the end, a fuckton of potentially great people get their feelings hurt either way.
great question. The alternative is not accepting 1000 applicants. Nobody said you have to keep up your job posting for two weeks, or two hours for that matter. stop once you have enough. Enough is defined by whatever number you would have filtered to. In the rare case none of the first ten applicants were appropriate, just open it again until youve got another tranche.
You are assuming quality applicants are evenly distributed in terms of time of application - they aren’t. If you cut off at 100, you will only get a sample of people spewing fully automated application bots which mostly aren’t what you want.
If your hiring pipeline is employing a filter that a) is not better than a random chance and b) is expensive to implement get rid of the filter.
Instead of spending all those resources on resume filtering, hire resume blind. Instead of using llms for a thing they are bad at (subjective decision making) use them to build a deterministic process that isn’t.
Use work sample hiring as the filter. Make the work sample automatic to sign up for and judge.
>instead of just interviewing 10-20 people and just pick one
Here's a realistic proposition. HR just wants to inflate numbers so that they seem busy looking for the right fit. Keep posting open for 1 week, manually filter for another week, invite people, employ one. Plenty of people with degrees looking for jobs right now, I don't see what's the issue with just trying one. Companies desperately look for the "magic" applicant that checks all boxes, while also trying to pay them almost minimum wage.
The goal for the interviewer is to have a much higher ratio of good/bad candidates after the first screening. This means the more costly time you spend on the second step has a better return.
One of the first things you do when hiring is to set a period and randomize order of resume when reviewing because early application is not a strong signal.
there have got to be better ways to optimize pipelines. maybe set a limit on number of applications for a role based on the number you/your team can reliably go through them. if more are needed then open the role for another wave of applications.
This word (determinism) has a magical effect of warping any online posts it touches. Once you hear it you can almost guarantee it's going to be misguided. At least this time it's actual determinism (same input = same output), not arbitrary unrelated things.
Determinism matters for reproducibility, but do you really want these outputs to be reproducible in this particular case? Making LLM outputs deterministic is relatively trivial, you have to use batch-invariant kernels (if you use batching) and either set the temperature to 0 (don't do that, randomized sampling is here for a reason) or fix the seed (better). It's readily available in a few systems. But this won't make the result more useful, it will just obscure the fact that the agent is genuinely not sure about it - look at the range of the scores it gives! It still won't predict anything but the score will stay the same each time. Do you really want that?
What happens here is they're supplying too little information (just a resume, which is almost at the noise level) and expecting a reply with too broad implications. This is a basic design mistake regardless of whether it uses LLMs. All surveys, tests, laws, and voting systems are extremely sensitive to framing because they work off too little information. But they also don't exist in vacuum, unlike this thing.
This. Human judges and examiners are famously not deterministic even though we would wish it were so - we've probably all heard the thing of harsher sentences being given in the hour before lunch.
I think what's more worrying to me (if other systems work like this ATS) is that it seems to judge based on a bunch of factors that will probably disqualify a ton of decent to good participants.
For example, 65 points are given for a mix of personal projects and open source contributions. Which is great if your one and only interest is in tech, and you don't have a family, dependents or a second/third job. If you have any of those other things, well the odds seem like they're incredibly stacked against you.
And it makes me wonder how many of these systems are stacked in favour of wealthy people with a near special interest level of obsession with tech and no worries outside of going to college/working a single job in their industry of choice.
Here's the brutal truth about life: it's not inherently fair and no one really cares about your circumstances. That means many people will be able to do things or have opportunities that you cannot have. That doesn't mean you have no power however that you need to make a conscious long term effort to get that power one piece at a time.
I know that some think this is just some cold hard straight talk but this style of individualistic thinking lacks empathy. And more practically, it’s a trap.
In context, the “doing things” and “opportunities” that we’re talking about are jobs, careers. So by promoting the idea that one must work harder or longer to get or keep a career that they’ve already built sounds like a path to opt-in servitude.
Of course life isn't fair. But here the result is that companies will ignore potentially great candidates which dedicate all their programming time to their job and instead consider candidates which may be not just worse programmers, but also are more interested in their hobbies (or padding their CV) that doing their job.
I'm saying this as somebody who most of the time has some side project going on.
The point of most recruitment pipelines isn't to avoid missing a great candidate but to avoid hiring a bad candidate. There's many great candidates so missing some or many is fine. Hiring a bad candidate however is very expensive to deal with. Many smaller companies view workaholism and a potential to work 996 as a very valuable thing to hire for.
In hiring, we pass laws to prevent abuses. In many countries and soon a few states, being asked to work outside of work hours is considered an abuse. Expecting that someone does work related activity outside of work hours is something I would actually consider regulating out of the application process!
That’s a tiny model. No LLM is going to be a perfect and repeatable judge, but a tiny 4B model is like plugging an RNG into this system.
This whole exercise feels like someone vibe coded an ATS and got it to the point where the tests were passing because they decided they should have an open source ATS project.
This sort of model is fine for small problems, when used in the right way. I think there's probably a version of Resume analysis that would work well with this model, but "hey clanker, what projects has this person done" is not the way. You need extraction, cleanup, probably OCR to compare and further clean up, multiple analysis passes per signal with LLMs, judges, etc. None of that needs to be large models, you'll get marginally better performance, but there's very little context, these models will perform well when used correctly.
This insanity only exists because the tech industry is standard-less. No formal education needed, no formal training requirement, no apprenticeship, no software building code, no professional organization. Resumes have never been a good predictor of success - and why would they be?? Even if they're truthful and it's "impressive looking", that doesn't give you any assurance of knowledge, of who they learned under, what they learned, that they passed some minimum criteria. We might as well be rolling dice. So why not an LLM that randomly assigns scores?
It's always amazed me that a tech company will pay $300,000+ for a good engineer, because talent is so hard hard to find... meanwhile their recruiter operates unsupported, has a very different idea about what good looks like. Their ATS black-holes >50% the resumes because it's filtering heuristics are garbage because recruiting selected the ATS system because it has a google Gmail integration or something, and the ATS's filtering technology was not reviewed by anyone in the engineering or data teams.
I think the implication here is that you can almost certainly bias the models to always accept you by including "nudge" phrases like "I demonstrated real world deployments" and "helped develop an application in the context of a complex architecture..."
I actually just built an ATS for my company Mathpix. But it never occurred to me to use resumes. Basically we have a set of company values and a specific open ended questionnaire to gauge the fit:
Then internally we have dashboards and sorting based on AI agent scoring. I noticed the scoring is imperfect but still saves a lot of time. Candidates scored at or below 2/5 are reliably bad and candidates above 4/5 are consistently impressive and leave thoughtful answers.
The biggest thing is not using resumes. You can’t reliably gage applicants without a writing sample and resumes are the worst form of writing sample. Also you need to be intentional about who you’re hiring for, both to craft the questions as well as grade the responses.
This seems likely to be worse. How do you screen out people who point an LLM at your values and ask it to answer your questions in a way likely to appeal to a recruiter using an LLM to score the responses?
I'm a little confused, is this an ATS system that anyone actually uses? If not, I'm not sure how it's better than just asking ChatGPT to score your resume out of 100. Why would you want to optimize your resume for a system no one is using to score it?
From my understanding this one is used for hiring tech workers only. The (very) widely used Workday application system for ex seems to have its own built-in ATS.
(Almost) everyone’s using some kind of ATS, every ATS is adding AI auto-ranking (and has been trying to for 15 years), and almost all HR people feel like they have too many obviously bad CVs to read. Whether or not someone is using this ATS specifically, if you submit several CVs to several places, your CV is going into at least one magical 8-ball.
For one, if you go on to Hacker Rank's "Screen" page, they mention the product is used by Stripe/AirBnB/LinkedIn/Atlassian/IBM etc etc. I imagine that there's plenty more companies using it too.
But I'd also assume that their competitors are doing something similar so I don't think we as an industry can just ignore that it's happening.
I've done similar things and used GitHub Copilot to scan a folder of 40 CVs and rate them -> I then review the top 10 CVs and comment on every rating whether I agree or disagree and why -> I then asked AI to re-rate all the CVs according to my comments. -> I then reviewed all the CVs against their ratings; the AI did a much better job for that 2nd round after its learnings.
It took more time than if I just reviewed the 40 CVs myself, but that was an experiment, and I think it shows the AIs can be trained on your comments. And if there is enough training and a good knowledge system that allows AI to apply the learning in those trainings, it can eventually become a lot more accurate at this task?
The takeaway from this for me is that, using an LLM to score anything takes multiple (maybe even many) runs and the result you’ll get is, at best, a sane-ish distribution.
Which sort of sounds workable until you scale it up to larger datasets, where at some point compute/time/energy costs will render it non-viable.
I am sure there’s some reasonable rule of thumb estimation on distribution that could be applied based off fewer runs per data artifact, but you’re always going to be trading off against confidence by doing this.
Beyond this, I’d bet that almost no implemented systems that use LLMs for scoring, ranking, or decision making use such a multi-run approach. Partly because people don’t understand their behaviour is stochastic, perhaps because a lot of people without a background in statistics don’t understand what stochastic actually means, and no doubt partly because of budget concerns: if you have to ask an LLM to do the same thing 10, 50, 100 times to get a sufficiently good result, then the cost saving argument is either weakened or completely destroyed.
There is at least one more aspect worth considering in the specific case of resumes/CVs: is the inconsistency of scoring by LLM worse than the inconsistency of scoring by a human following a similar process?
Because the reality is that, even for an experienced recruiter, reviewing hundreds or thousands of resumes or CVs gets pretty fatiguing. People get hungry, bored, tired, restless, irritable, etc.
That inevitably leads to inconsistencies creeping in, so there’s always an element of “luck” (or, perhaps better, uncertainty) as to whether your resume/CV passes screening.
So is that inconsistency better or worse with LLM screening? I don’t know. But, at least, if it’s not worse maybe it doesn’t matter for this specific use case. And if it’s notably better then maybe it’s raised the bar on what “good enough” screening looks like?
(And I’m sure other use cases warrant similar, “does it matter?”, questions, with the answers no doubt landing differently.)
My experience with benchmarks and evals is that it can take ~20 runs of a problem for the distribution of answers to start to converge. Ideally you'd know the convergence properties of your algorithm ahead of time and make a Bayesian solution that makes the uncertainty explicit.
> I don't understand why they would omit these factors from the evaluation.
Only hiring MIT graduates sounds great to a lot of tech folks! Automatically rejecting applicants from HBCUs, however, sounds like a lawsuit
As to GPA thing, I think it's just to stop the LLM glomming onto an obvious numerical grade? LLMs like to rank things by obvious dimensions, and whether someone had a 4.0 or a 3.8 in grad school makes very little difference to their performance 10 years down the line.
I don't understand why they'd hand over those data points over to the model in the first place. If it's in the context window, it's impacting the output. To ensure that no weight is placed on those factors, they should be sanitizing them out before handing the data over to the model.
Hopefully so that people like me, that dropped out of high school yet have had a successful career as a self-taught engineer, have a chance. [1]
Just kidding, my resumes are sent to /dev/null like everybody else’s.
——
1: In fact, I will be controversial and say that self-taught engineers tend to be the strongest in their own particular niche, because they are powered by sheer desire to learn and improve. I am routinely appalled by how many people go on forums to ask how to learn a new thing, completely unable to self-direct their learning. I blame the modern school system.
I'm a self-taught programmer as well, who dropped out of university, and these factors being omitted would benefit me as well, but I feel like good grades and a good university are still indicators of someone being or is capable of becoming a good programmer.
This system would drop a Harvard top graduate for someone having a year of experience in some outsourcing firm.
Unfortunately, graduating from Harvard is a very good predictor of whether your parents were wealthy, and also that you are less likely to be black.
I worked for a very large job board for the last six years, it's the one you're thinking of. What we found is that the outcomes of paying attention to what school you went to are almost entirely discriminatory, and not predictors of success.
> I feel like good grades and a good university are still indicators of someone being or is capable of becoming a good programmer.
Really depends on the program. In my undergrad program there were some very smart CS students who got great grades that really struggled with the programming. Smart and capable people can be bad at programming and lack many qualities that make for a good hire.
I started in an outsourcing firm (body rental actually) but I definitely get your point. Maybe they optimize for real world experience, or rather, how one is used to workplace politics and logistics. The top grad will have higher expectations, and all they want is a cog for the Machine.
HR market is basically an early google rigging era, where you can place hundreds of keywords at the footer (white text on white background) to start popping up on random searches.
I have been at both side of the market. And it sucks so bad at both ends. Companies which deeply care about next hire are struggling to hire and actual great people looking out are outcompeted by AI slop and AI bulk applying.
The mind blower is that this spam and slop is just lowering the job market to the quality of every other capitalist market. Poor hiring manager has to look through 1000 applicants, 950 of which are spam? How many ads are shoved down your throat every day, and how many products are you actually looking for info on?
A better way to reformulate this problem is for the LLM to be tasked with making a _comparative_ judgement between two CVs. This should prove much more reliable, especially if you give it a third “too close to call” option. You can also ask for clear justifications of preference.
The only drawback I see is that you should compare every pair of CVs for best results, and that grows quadraticly with number of CVs.
Of course you can settle for fewer comparisons and not perfect results. But then I'm not sure if you can hit a good ratio of quality and token spend.
Could probably do an elo system and sample pairs. E.g.
1. Set the elo of all CVs to 1000 elo
2. Randomly pair up CVs and compare. Winners gain elo, losers lose elo.
3. Repeat #2 for a few iterations, then remove bottom X% of CVs.
4. Repeat 2-3 until the amount of remaining CVs is small enough to do an exhaustive comparison.
I don't have a mathematical proof, but I suspect that this is a decent cost-effective approximation of comparing every pair (depending on the parameters)
> you should compare every pair of CVs for best results
Or compare each one to a reference set? Take 5 resumes of existing employees, rank all candidates against that set, maybe you get some useful level prediction into the bargain
Why doesn't something like this exist for real estate? A popular open source AVM (automated valuation model) that helps home sellers get an idea of what their home will sell for.
Right now it seems AVMs are mainly seen as just a way to capture leads. Every estate agent will tell you they have some magic recipe that makes their valuation better than anyone else's.
I have had a bunch of ideas on how to approach this, but I really could do with a collaborator or two.
Disregarding the fact that this thing is completely broken, its grading rubric is ridiculous to begin with (as was mentioned in the article itself, but I must reiterate how completely stupid this is):
> 35 points for open source contributions
> 30 for personal projects
I don't contribute to open source or have personal projects because I don't spend my free time doing what I do 40 hours a week to make a living. My 15 years of work experience is worth a maximum of 25%, so any company using this idiotic system would pass on me immediately. Open source and personal projects are fine, but in no sane world are they worth 65% of a resume's score.
They are selecting for people who are fine working in their free time. If you contribute to open source you are more likely to contribute to the company on weekends. If instead you have other hobbies or a family that takes up non-work hours you are more likely to drop your pen after forty hours.
Maybe they're selecting for intrinsic motivation. People who enjoy programming to the point they do it for fun, not just because it pays.
Free software work doesn't imply we work for free. We work on our projects, the stuff that we actually enjoy working on. Nobody is going to work on corporate products without adequate compensation.
If the open source work is part of a hiring pipeline, sure. Contribute to some repository and have it serve as a resume that gets you hired is also a form of compensation. If the work is also enjoyable, then it's a win either way.
You might have numbers on that but after working in a place with a strict no more than 40 hour policy my view is that people overwork for many reasons. Being an open source enthusiast is not one of them.
> If you contribute to open source you are more likely to contribute to the company on weekends
I wonder if that assumption is bourne out in reality though?
I'd imagine if someone's OSS contributions are enough of a factor that it's worth hiring them, they're not going to drop it on a whim to work extra hours on the day job.
(Assuming you weed out open source contributions like "I made a todo list app in React but licenced it as MIT" or "I fixed a typo in the docs for NextJS". )
I really dont understand this constant changing of numbers. I have tried a bunch of ATS reviewers and everytime on the same resume i get different numbers. Its weird and unreliable. I understand the need for doing this to filter through thousands of CVs but maybe there is a better way. Like a take home test at the beginning or a test of somekind.
I would say people that hink the LLM is doing a better job than they are in for a treat. I did expect the resulta to be of the same quality as if a human does the job - it averages out and has a big error margin.
Many em dashes and a "This is not, it is…" later, I think this article would have been a much better critic if it didn't use a LLM to (re)write some parts of it.
I always find it funny when a technical crowd starts picking on em dashes as a sure sign of AI. I mean, are keyboard shortcuts really that difficult for developers? Some of us always knew how to use correct punctuation, even before LLMs existed.
Also, neither "this is not" or "it is" appear at all in the article?
He tried with a tiny model (gemma3:4b), got a range from 66 to 99. Then tried again with a small model (gemini 3.1 flash lite), the range was 48 to 64. Would a frontier model be more consistent? Perhaps this tool was optimized for more capable models?
It makes sense to me intuitively (though I'm not sure if my reasoning is actually correct).
Worse model may not "know" enough to distinguish between a 70 and a 100 candidate, so it's expected that it's output has high variance. But a better model might "know" enough, so it can be more confident and thus more consistent.
I feel like hiring is all a bit broken. Roles get flooded with applications, it's chance whether your CV gets through, then there's hiring rounds that seem designed to make you quit the process before they have to filter you out.
I'm on the other side, and my main tip (at least if there's people like me!) is: avoid the usual AI signs.
For one role we got ~70 applications and all CVs looked obviously AI-written. I don't know whether the people did actually do any of the things mentioned and I don't have the time to find out, so the AI-written CVs are a discard-signal for me. (Either those people delegated a very important task to AI and didn't even bother to check, or they are bad using AI and don't know -- I want neither)
Any CVs that signal they were actually written by a person I will actually look at.
> For one role we got ~70 applications and all CVs looked obviously AI-written.
Were those ~70 applications all of them, or were those ~70 applications the result of an AI filtering from a larger amount?
If the latter, are you sure your AI is not filtering out the hand-written CVs and giving you the ones that have been AI-assisted or AI-written (with or without "the usual AI signs")?
if i ever go back into the job market, will need three accounts: Peter J Smith, Peter Smith and PJ Smith. they live in #101, #102 and 103# 5607 Jane Street
ATS resume usually check the keywords, and formatting your spacing and give score accordingly. As If someone is following some reference of the format. It can depend might he will be getting low scores.
I thought this was only an old urban legend; some people actually use this technique? Especially in a trade supposed to be led by people trained in sciences?
Given how often it's been mentioned here, it's likely that this is an urban legend that people are pretending to have first-hand knowledge of for karma. In a trade that's supposed to be led by people trained in sciences, no less!
(A more charitable interpretation would be that aforementioned CTO was making a joke that didn't land.)
or its so old, people would make the joke and interns would repeat it unwittingly. no one has to consciously be lying for this type of meme to continue spreading.
Looking at the linked scoring prompt (resume_evaluation_criteria.jinja) [0], I immediately see several red flags that suggest the output won't be reliable. (I'm developing an LLM intensive application where the stakes are high enough that I need the LLM output to be reasonably correct.)
1. The prompt is trying to get the system to do all of the evaluation steps at once. Instead, the system should break down the task of resume evaluation into its subcomponents and have separate prompts for each component. Like "evaluating open source contributions" should be its own task. Same with "assessing the complexity of software projects on the resume." Fwiw, each of the tasks contained within the prompt is woefully underspecified.
2. The prompt leaves spreads of ~10 points up to the LLM, when it's doubtful that humans are that well calibrated. Take for example:
> SCORING CRITERIA Open Source (0-35 points)
HIGH SCORES (25-35 points):
- Contributions to popular open source projects (1000+ stars)
- Significant contributions to well-known projects
- Google Summer of Code (GSoC) participation
- Substantial community involvement
Are all of these 35-point examples? Is one a 26-point example? If not, what's the difference? If an expert can't reliably make the judgement, the LLM is going to struggle too. One partial fix is to get rid of the ranges and just say all of these are worth 30 points. An additive point scheme would be better...
3. The authors of this prompt have left an incredible number of judgement calls up to the LLM, when that's the very thing you want to minimize. Using the same example as above...
- Are all contributions to open source projects with 1000+ stars equal?
- What counts as a "significant contribution"? Doesn't that imply that the LLM has to know or read through all of the commits in like the last ~6 months at minimum for the project to understand what the given contribution meant to the project? That itself isn't impossible with tool usage, but again, that'd be a separate task.
- What on earth counts as "Substantial community involvement"? Why didn't the prompt authors define this, or at least give a few examples?
Honestly at this point maybe someone should build a tool that scans prompts for adjectives...
4. This sort of thing is just asking for trouble:
> SCORES MUST NEVER DEPEND ON:
Candidate's name, gender, or personal demographic information
Just remove this stuff before you send the rest of the resume to the LLM. Even if you ask it not to, it's not a person, it's a very fancy statistical distribution generator. All of the input (including the name) will affect the distribution that gets generated. (This one is not unlike Andreessen's "don't be a sycophant" prompt.)
5. Obviously this one depends on the LLM in question, but instead of writing things like:
> DO NOT RETURN A RESUME SUMMARY. RETURN ONLY THE SCORING EVALUATION IN THE SPECIFIED JSON FORMAT. Analyze the following resume and provide a JSON response with this EXACT structure (all fields are required):...
The system should utilize the "structured output" option, which guarantees a fixed output format. Also, fwiw, the JSON should force the LLM to pick between categorical options as much as possible. Forced-choice structured output should, at least in theory, cut down on hallucinatory responses and constrain judgement calls.
6. One major thing that's not in the prompt is anything about traceability. This system should be designed so that humans can review the logs and make sure this is working as intended.
7. Another thing that is missing in the file is what I'll call evidence of a theory of coding / coder quality. Most of the examples are designed to have the LLM assess proxies for code quality, not code quality itself. Surely both should be taken into account?
I'm not an expert at evaluating coders. But two pretty basic LLM-answerable thing I would ask is: How well do a candidate's 5 most recent commit messages match the contents of those commits? Do the claimed technical skills on the resume match their GitHub code? (i.e., if they say they know R, is there any evidence of that on their GitHub?)
8. The prompt also seems unaware of what it's asking the LLM to do:
> LIVE DEMO BONUS: Projects with working live demos should receive 10-20% higher scores
This implies that the LLM can use tools, but even then, I'd be pretty wary of its ability to fully execute this part of the prompt without more detailed instructions, examples, and guidance. There are very likely tons of edge cases here.
With such kind of ATS systems, is it still a thing to optimize for a one page resume that is easy for a human reviewer to scan, or just include enough buzzwords and external links to try and please the LLM?
I wouldn't assume based on this one thread/article that this is what you need to optimize your resume for. Nor that a majority or even significant group of reviewers is even using LLMs. I've been involved in hiring pipelines and never even thought of using LLMs to review incoming candidates.
However given the time constraints reviewers have, yes, the former (making a resume easy to consume quickly) is a huge help.
I wonder how is this even legal? The only useful job the HR departments are ever required to do - they decide to automate it? Aside from being a daycare for adults, what exactly does HR accomplish? It's clearly NOT on the side of employees, but this seems like they're clearly NOT on the side of employers, either.
While resume's are being filtered left and right, they just make TikTok's on company's dime [1]. What a sad state of affairs.
They could be using this just to throw out the obviously bad CVs, and then manually go over the rest. I'm not sure if they do this in practice, but the tech itself can be useful.
Also if HR was really useless (or actively hurting the company) they wouldn't still have a job (or they'll lose it eventually). No one likes burning money for no reason. So obviously they are doing something useful.
The last time I heard HR being completely let go was with a fintech company Bolt. Then again, that company was midsized, around 200-500 people or so. For larger companies, it's going to be difficult to even realize that HR is redundant in the first place.
I was inspired by this. I made a Claude skill to take my resume and compare it to any job description to point out viability and gaps. Pretty cool skill. I'll post it somewhere.
This could be used as a good way to self-evaluate one's current position from the company's point of view. you would tweak prompts and guidelines that are expected from the company and see how you score
I sort of hope we land on 2 agents, one working for the candidate and one for the employee do a screen round. Salary compatiability could be negotiated by a 3rd party bot that knows both parties ranges and what would be needed each end of range, and figure out yes/no worth going ahead. Such a time saver.
This is just the 'LLM judge', very badly implemented without any scientific prudence. What a joke. To be terse: you cannot rely on LLMs to provide standardized scores against arbitrary criteria. To get close to 'reliable' you would need highly tested rubrics, grounded in human decision-making, and you'd need to avoid all the measurement biases these things are riddled with... positional/order effects, anchoring on whatever numbers you stuffed into your own prompt, scale-format sensitivity (a 1–5 and an A–E scale give different answers for the same input), holistic-vs-isolated context effects, and lovely examples like where adding a "be unbiased" instruction makes it more biased. I've studied this at length. You cannot even _begin_ to approach this problem seriously without held-out validation, inter-rater agreement, and ground truth. This repo is just quagmire of wishful vibes with random numbers littered throughout.
It seems like the design is flawed, probably because the scoring structure and conditions are wrong. And originally, due to the nature of LLMs, even if the input is unstructured, when you design something like a RAG system, you usually need to create a verifiable evidence table. Even with that, the scores are still probabilistic by nature, but at least they stay within an error distribution that I can verify. But it doesn't seem like there's any such evaluation criteria here.
Typically, retrieval should be tied to evaluation metrics, evidence should be linked to scores, and you also need to account for parsing errors.
But personally, I'm weak to these kinds of ATS systems (ugly appearance, non-native English speaker, didn't go to a good university), so if this kind of filtering existed, I probably would have never had a job in my entire life. Come to think of it, even now I don't have a proper job—I just bid on projects at the lowest price and implement them. So maybe it doesn't really matter whether such a system exists or not
I’ve always assumed any LLM output that was some type of rating or score was bullshit. Unless the LLM writes a Python script to calculate the score (and even then…) then the score it outputs is just the next most likely token, taking into account temperature and what not.
You see a lot of frameworks for things like spec-driven development make use of scoring how good the spec/design/plan is and it’s like, uhhh…
The true test of HackerRank is can you setup a system that combines a document editing / paraphrasing LLM with gradient descent on the HackerRank LLM to turn your arbitrary resume into a reliable 120 out of 100.
One of the weird properties of other people using LLMs is the potential of having oracle access to your opponent. Even if you don't have their exact LLM a good guess at it may be a better model of the opponent than you ever had before.
There's a whole lotta analysis and math and bar charts.
But the big question I want to know is "Why did I score that?" And these slop machines absolutely cannot explain anything. That's the root problem with LLMs as a whole. There is no way to describe WHY an llm makes a decision.
Was it because they are a woman? Does the woman's name have more pregnancies than other names? Was it because their job history make the person older (over 40)? Is the person black or black name? Is the name or address attributed to higher criminal tendencies?
But no, you font get to know ANY of that. Slop machine says 66/100 , if you're lucky to even get a number. Usually its a 30 second rejection, or rejection at GMT 0:00 when the batch is processed and you summarily failed.
It's a good analysis but the AI slop writing makes me not trust you've reviewed this and I'm unable to finish or subscribe. I'm sure you're a great blogger but this is holding it back!
> LLM is called six times to extract structured information
Followed by
> The default model is gemma3:4b, running at temperature 0.1 — low, supposedly nudging the model toward deterministic outputs.
This is exactly why hiring is even more broken: Because the people looking for candidates are also just as unqualified if not, more.
Using much weaker LLMs to replace the person in charge of the final judgement call is the wrong solution as this is a plain old social problem.
Even if you wanted to use LLMs for this case, the default configuration, model choice is laughably flawed. This LLM can’t be trusted as it doesn’t even know what it is reading.
The correct solution is either advanced OCR with keyword ranking with a basic filter or a far stronger LLM that excels at document / vision parsing benchmarks with an experienced person making the final judgement call in case the technology misses a critical detail.
Rather than using this less accurate one that hallucinates out its decision depending on a dice roll.
This actually makes a lot of sense, it's testing the luck of the candidate through the rng feeding the LLM. You wouldn't want to hire unlucky employees after all! Hiring managers of the past would solve this by throwing every second resume in the trash, now this is a built in feature of ATS.
So many people are willing to participate in this kind of robotic practices in human employment makes me think that many are starting to consider that this is as unavoidable as global warming and rather play along, adapts their career (life) to it, sculpture it towards a specific look, doing things that will give them point on some arbitrary test run. Which I feel being dangerous, leading to superficial minded workforce, not those good in something, including judgement of a problem and solution. But good at manipulation.
>You might as well throw out half the resumes and tell the the applicants you don’t fuck with bad luck.
Hmm, well, maybe a bit with a nuance of elite class structure reproduction (that doesn’t prevent a few transclass to showcase in case anyone critic the perfect meritocracy at run), that’s basically what people get, so crude truth but truth nonetheless.
Oh don’t take it personally. Your own bespoke hand-tailored process of course is different, it does give the opportunity to everyone to reach the most accomplished version of themselves beyond what they ever dare to dream.
It won’t help though with the systematic failure of aiming to provide an accessible path to flourish for everyone and letting no one behind.
Again, this is no fault of any specific player, but as long as a majority feel compelled to move within the frame of the game with few winners that merit all they got in contrast to large stock of inept losers, the outcomes are no wonder.
There's no better feeling than building something open source and watching it take off. Nine months ago, I built a simple hiring agent to solve one very real problem.
Things it is not: It's not an ATS. We don't use it to screen our open roles. Our customers don't use it either.
Here's what it is: Every year at HackerRank, we get 50,000 to 60,000 intern applications. No human can read that many resumes well. So I built something to rank them, helping me decide which resumes to read first.
[This was before we built AI Interviewer (Chakra) to automate the first round of interviews, so candidates are no longer rejected based on their resumes alone.]
Two things worth clarifying since I've seen them come up in this thread:
The default model is gemma3:4b because it's what runs locally on most laptops - no cloud API needed. Actual resumes are evaluated using a top Gemini model. The repo ships with a demo config, not the production one.
The cutoff score was set very low — the system was designed to rank resumes, not reject them. Only resumes at the very bottom of the distribution were filtered out. The vast majority passed through to human review, where the real decisions were made.
Over the last week, it's taken on a life of its own. People are cloning it, running their own resumes through it, opening issues, sending PRs.
I contributed to open source a lot in college. Somewhere along the way, I drifted away from it. This week reminded me how good that feeling is. This thread has also given me more ideas than I expected. The critiques here are sharp and I'm already thinking about how to act on them. Improvements are coming.
You know you're not writing for LinkedIn? So platitudes about drifting away, watching your project "succeed" by being really popular, is not relevant to the main concerns pushed by this piece. Particularly brushing off the non deterministic score calculation.
I'm a bit disappointed to see "The critiques here are sharp", a Claude tell, in a response which (to me) is trying to subtly argue that hackerrank is not overly reliant on LLMs.
I'm not sure if your intent was to come across as having written this yourself, but it did not have the effect of improving my perception that this approach is flawed.
I was also disappointed that you didn't address the variability in scores. I'm inferring that you believe the larger model takes care of the main observation in the post, but I don't really see you directly addressing the points.
There is variability in scores and that's expected given we are eventually using a LLM to score. At least, when I used it 7 months ago, the only way I could avoid it was by keeping the cutoff score low (as low as 10 or 20).
Reading this thread, I'm hoping to minimize the variability even further (even though I know it can't be fully removed).
Saw this comment at the top with 0 replies and thought “How is that possible??” and then saw the “0 minutes ago” timestamp. Only on HN can you stumble into the comments section just moments after a CTO, founder, author, etc. left unfiltered remarks about the exact topic of the post. Never change HN.
And this + the tendency for AI to "prefer" AI produced code + some other AI biased is why *this is most likely highly illegal to use in the EU due to violating anti discrimination laws in multiple ways.
To be clear:
- randomly filtering "too many" resumes is pretty much allowed (I think)
- but must be actual random independent of the resume (and can be in multiple layers, i.e. random filter > pre-select > random filter > select)
- this isn't the case for AI as the random aspect isn't done as the random aspect is not independent of the actual resume evaluation
- in general you can't make sure the AI doesn't apply systematic biases, and there is high indication that it does do so
- for humans you can train them and order them to ignore their biases, this won't work reliable either _but now you delegated the responsibility of illegal biases to the hiring personal violating the order_. But for AI usage you are responsibility no matter what you tell it. Lastly you can technically "show/proof" a specific used AI is highly biased in a specific contexts, which for human employees is technical possible but practical not really practical. So this moves "specific mostly deniable" cases, into "systematic proven bias" teritory. Or in other word legal risk goes from "limited/no issue" to "people can systematically f-you over if they know you use AI for hiring".
An alarming number of people don't understand that LLMs work via purely stochastic processes, so I'm happy to see in-depth pieces like this. I'm looking for a job and maybe this is why it's so hard to get a callback these days: resumes are just dumped in some LLM black hole and no one really knows how it works. The author says:
> temperature 0.1 — low, supposedly nudging the model toward deterministic outputs
This is not correct (and is briefly touched on later in the piece when he sets temperature to 0), temperature is not some kind of "deterministic" switch, but rather it affects the sampling distribution (which becomes more "spiky"—but is still very much a distribution).
In theory, temperature 0 does make the LLM deterministic.
Well, in theory theory, temperature 0 doesn't really exist. Mathematically, as lim temperature->0, the distribution gets spikier and spikier, the most likely sample goes to almost-but-not-quite infinity and the rest go to almost-but-not-quite 0. In practice, temperature=0 is literally a separate branch of an if statement that just picks the most common sample (using the actual formula that works for non-zero values would cause a zero division).
However, due to things such as batching and even different kinds of floating point imprecisions for different algorithm implementations, the probability distribution itself often differs run-by-run, so what you sample from it also differs.
>in theory theory, temperature 0 doesn't really exist.
It does exist very much, even if you go to pure math. Look at the softmax function and take the limit as T->0. It becomes a dirac-delta function. I.e. in a discrete setting (like for LLMs with a finite set of output tokens), probability P becomes one for argmax and 0 for everything else. Only in coding practice it is easer to implement T=0 as a simple if check that directly chooses argmax instead of calculating the limit of some function that includes 1/T quotients. But setting T to zero is in both, theory and practice, turning the usual probability function into greedy sampling.
> Look at the softmax function and take the limit as T->0. It becomes a dirac-delta function.
In pure math, it does not always do that. It becomes a dirac-delta comb with equal weight on every maximum. There can be more than 1 maximum. Setting the temperature to zero turns into greedy sampling, but greedy sampling is not necessarily deterministic as you can have multiple equally optimal options.
That is not a problem for LLMs, because in practice floating point inaccuracies (in particular after exponentiation) prevent values from being exactly equal. That's why greedy sampling generally produces deterministic output for LLMs. The real gotchas are elsewhere (like with batch inference as we've seen with earlier GPTs). But unlike what the earlier comment says, this is a non-issue mathematically.
> That is not a problem for LLMs, because in practice floating point inaccuracies (in particular after exponentiation) prevent values from being exactly equal
Any two tokens ending up with the exact same logit is very unlikely, but not impossible; and as the number of output tokens grows, the odds that it will happen eventually gets higher and higher.
I suppose, to ensure determinism, rank by logit then token ID, so you still have a deterministic winner even if occasionally two tokens get precisely identical logits.
> It becomes a dirac-delta function. I.e. in a discrete setting (like for LLMs with a finite set of output tokens), probability P becomes one for argmax and 0 for everything else. Only in coding practice it is easer to implement T=0 as a simple if check that directly chooses argmax instead of calculating the limit of some function that includes 1/T quotients.
I don't understand the distinction you're drawing. A Dirac delta function is a "simple if check".
The point is that the case T=0 doesn't just "exist" as a special code branch - it is still well defined mathematically without any change to the output function. What the above comment refers to with the extra "if" check is just a limitation of computers not liking to divide anything by zero, even if the actual function exists and is well behaved at zero. It is not some weird or special theoretical construction.
> However, due to things such as batching and even different kinds of floating point imprecisions for different algorithm implementations, the probability distribution itself often differs run-by-run, so what you sample from it also differs.
Exactly. While I’m assuming this won’t be news for most here, for those that are still new and/or curious about some more explanation on e.g. the floating-point imprecisions, see this nice article: https://thinkingmachines.ai/blog/defeating-nondeterminism-in...
> Mathematically, as lim temperature->0, the distribution gets spikier and spikier, the most likely sample goes to almost-but-not-quite infinity and the rest go to almost-but-not-quite 0.
That's not how limits work. As the temperature goes to 0, the rest goes to 0. That's it. The "almost-but-not-quite" is part of the "goes to".
Let's say f(x) = 3x+1. It's a continuous function. If we let x go to 10, f(x) goes to 31. Not "almost-but-not-quite 31". No, to 31. (If you don't have a continuous function then it's the same argument, but less intuitive to illustrate.)
> However, due to things such as batching and even different kinds of floating point imprecisions for different algorithm implementations, the probability distribution itself often differs run-by-run
The implementation does not often differ run by run.
> The implementation does not often differ run by run.
If you use a cluster, or even multiple clusters, and they have non-identical hardware, then two consecutive runs could end up being routed to nodes having different GPU models with slightly different floating point behaviour, or even software differences (e.g. newer GPU offers some feature usable to speed up calculations which older model lacked; same code can use the feature when it is available, fall back to slower alternative if it isn’t). The larger your scale, the greater the odds it will happen
If you make an exact integer implementation and run with temp=0 it's deterministic.
You don't even need temperature 0, just make a random seed for the sampler part of the input and then its deterministic as a function of the input.
But running autoregressive models at temp=0 tends to expose pathological behavior, because the training process produces a function with a lot of gain so its prone to feedback on its own noise.
As I understood it, the "randomness" affecting what is selected at any temperature still comes from a PRNG or CSPRNG (or whatever RNG you want, maybe a hardware one), and if you where to swap out that with something deterministic you'd get the same results every time (barring non-determinism in other parts of the OS/drivers/maybe even hardware).
But theoretically, the output of every LLM is seed-driven (or could be if you wrote the software to isolate it) just like any computer software. It's just none of the software written (even llama.cpp AFAIK) chooses to support stable-seeding due to the changes in stuff like CPU/Vulkan/CUDA/Metal differences making it difficult to make consistent.
They could though! Hopefully one day someone implements it into the mainstream LLM-engine software and it gets exposed in the APIs serving the models. It'd do a lot to show folks the "internals" of these models.
Stable seeding is not enough. A lot of modern, fast compute kernels are nondeterministic. Floating point multiplication/addition is not strictly associative and e.g. reductions can combine results from different threads in different orders (e.g. through atomic ops). You can write kernels to be deterministic, but it is generally less efficient.
It's probably due to the fact that it's a cloud service. You have no guarantee that your next request will go to the same machine. So even with an identical seed, and temp 0 you might get different hardware and hence different accuracy/noise in the floating point operations.
How can there be noise in floating point operations? I could buy like completion order for parallized batches i.e. adding a+b+c is different from a+c+b etc.
While the IEEE 754 standard ensures that individual basic operations are deterministic and strictly bounded, it does not guarantee that an entire program will yield bit-identical results on all CPUs.
CPUs and their execution environments introduce subtle hardware variations, architecture choices, and compiler optimizations that break bit-level consistency.
(same for GPU/TPU, ...)
PRNG is deterministic.
that's incorrect in the presence of batching. it's tough work making it truly deterministic:
https://x.com/FireworksAI_HQ/status/2069873437217276015
It's not that hard. What is hard is making it truly deterministic and retain high throughput.
To be clear, temperature 0 is deterministic and will produce the same output for exact duplicate inputs, across all seed choices.
Provided:
* If it’s MoE we are talking about, that the duplicate inputs are for the whole batch (yes, your batch neighbours can impact your choice of experts. Blergh.)
* Your kernels are deterministic
* There’s no system wide effort switch that responds to, e.g. work load across the cluster (for a thinking model)
Upshot:
Temperature 0 is not deterministic in probably any existing cloud infra, but it could be for edge inference pretty reliably.
To your quibble on 0.1 being more deterministic - I think it’s a pretty fair summary - we’re going to sample much more from the ‘temp 0’ answer at 0.1 than we would at temp 0.9, no?
Even then it's deterministic in the way a hash function is deterministic. Change one letter and you can get a completely different output. What people actually want is something continuous.
This is it. People mistake deterministic for precise/exact/correct. It's not.
A distribution with all probability mass on one outcome is deterministic, so in principle, setting temperature to 0 _should_ result in deterministic outputs. There are a few reasons it might not, but I don't think any of these apply when running a local model like the author did.
> so in principle, setting temperature to 0 _should_ result in deterministic outputs
It is a common misconception, but it is not true even in principle. If I have 2 or more logits which are equal to the maximum of my logits, I will sample uniformly random from them with any temperature, even zero. Sampling from softmax([1, 0, 1]) is still stochastic at temperature 0, because the limit is to sample uniformly from the first or the last element.
Anyway: "GPUs don't do deterministic matrix multiplications" is the biggest source of randomness in LLMs. GPUs put the associativity of the sums in matrix multiplications in arbitrary order, and this has a huge impact on the logits coming out of the neural network.
You don't have to sample uniformly. You could take the lowest index of all maxima. But yeah, the main source of randomness is non-deterministic matmul, and temperature does nothing with it
> "GPUs don't do deterministic matrix multiplications" is the biggest source of randomness in LLMs.
But this isn't a fundamental property of LLMs, it's just an implementation detail. It's pretty obvious that if you evaluate the matrix multiplications correctly and deterministically sample from the highest-probability outputs, you will have a deterministic LLM.
It may be an implementation detail, but in practice, if the only way to get a deterministic output is to run on the CPU, then it's not going to be usable.
Actually, Google's TPUs are also deterministic!
You can tell GPUs what order to do math instructions in.
> GPUs put the associativity of the sums in matrix multiplications in arbitrary order
That’s user-controlled too, not an inherent property of GPUs:
https://docs.pytorch.org/docs/2.12/generated/torch.use_deter...
The matrix multiplication is only deterministic for sparse-dense products under these settings:
> torch.bmm() when called on sparse-dense CUDA tensors
And it's not listed under the operations that raise an exception otherwise, so I'm not sure the docs promise that dense-dense matrix-matrix products are deterministic.
Oh, thanks, that’s interesting, I thought it covered that too!
There are. If the kernels are nondeterministic (e.g. timing issues) there are minor changes between runs, on a single system, even with eager decode enabled (typically what temperature=0 achieves).
Setting the temperature to 0 should give deterministic results but that's not any better - it's just hiding the huge variance by only taking one sample.
So you would get always the same result, but it could be the wrong one
Of course, nothing can guarantee the right answer from LLMs
I mean the easiest explanation would be that the model harness doesn't always take the most likely token but does top-k sampling or similar. temperatur just means that probabilities get more and more equalized, boosting the chance that an unlikely token gets picked. but even with temp 0 you could have 0.8 T1, 0.19 T2, ... and sometimes sample T2
No, this can't happen at temperature 0. The formula defining temperature-adjusted softmax isn't strictly defined at 0, but taking the limit (in the case where all logits are distinct) results in probability 1 being placed on the largest logit. Samplers will typically special case temperature 0 and pick the most likely token at each step.
This is a very authoritative answer that should be more nuanced and caveated as implementation-dependent. In some cases, repetition penalties take precedence over sampling; top_k and top_p can also be handled before or after the temperature step. In other cases, `0` is turned into like 1e-10 or some super tiny float value (which can drift if you do any arithmetic with it). Routing, quantization, etc. can also have an effect on sampling. And yes, in some cases, setting temperature to 0 can mean "pure greedy decoding" which makes the decoder about as deterministic as it can get.
A more spikey distribution exactly makes the distribution closer to deterministic. That's not the point though. Even in greedy (deterministic) decoding, it is still a black box though that reacts in ways ways that are unpredictable to the inputs. Switching one word around might lead to different scores for example.
Yeah, this is the forest that the people arguing about math trees are missing. It doesn't matter that the algorithm is deterministic if the algorithm passes the input through a cryptographic hash function to make a yes/no decision. The result may be perfectly reproducible and still non-sensical in its distribution with respect to its input domain.
Agree
> An alarming number of people don't understand that LLMs work via purely stochastic processes ...
I've been studying AI for 20 years. What really needs to be added to this statement is:
"An alarming number of people don't understand that LLMs work via purely stochastic processes - and so does human thinking. People do NOT arrive at the same conclusion if merely the weather's different. Worse: with human thinking not only do most people not think this is real, a subset of people will actively fight the idea. Of course, depending on the weather"
Every time people point out a limitation or constraint of LLMs, I see a comment that is to the effect of “but humans…”. I don’t understand why this comparison is relevant to this particular thread. Is it just an amusing similarity?
The same person is not going to give you three different answers within span of minutes. Especially when nothing fundamentally has changed. People might or might not update their views depending on their biases.
I'm pretty sure the personality tests are created specifically for the reason that a single person can have fundamentally (or conflicting) beliefs about himself in a matter of minutes. You can say "I am honest person" and the next minute you can say "I never lie" - and both cannot be true for an average person.
We expect computers to be consistent on the other hand. A calculator will always give you the same answer unless some chip gets struck by a particle. LLMs are on computers and should be fairly consistent too.
And this lies at the heart of the problem.
We expect computers to be consistent despite running programs that are not designed to be consistent.
This despite the fact that we have lots of experience of programs running on computers that produces wildly inconsistent outputs.
But for some reason some people choose to assume LLMs should act like a calculator instead of any of those programs.
> This despite the fact that we have lots of experience of programs running on computers that produces wildly inconsistent outputs.
The average user has very little. A word processor with inconsistent pagination or a spreadsheet with inconsistent totals is rightly seen as faulty.
Yeah but daily tools have lots of complexity which appears as non determinism (if we are thinking only UX, not actual determinism). For example, try moving an image in the word doc. I have been using MS word my entire life it seems, still don't know what the rules are lol.
You're using a mouse? I have no problem getting reliable output from reliable input - through keyboard.
What's even worse, different humans have different weights.
If you train two different LLMs and replace what data they "see" in batch n, that doesn't affect the data they see in batch n+1, or any further batches. In LLMs, you can introduce "noise" into the training process, but that noise doesn't really compound.
Humans learn from experience, not from data, and their experiences at age n shape what experiences they seek (and hence train on) at age n+1. A small amount of "noise" injected into their "training", let's say hearing a group of friends discuss a movie while their identical tween goes to the bathroom, can compound into them watching that movie, which can compound into them forming an identity around that genre, and so on, until they're two completely different people, trained on completely different "data mixtures".
> What's even worse, different humans have different weights.
Far worse would be different humans having the same weights.
Test retest reliability is a thing in psychometrics.
Ah cool. So there is data? How consistent are humans?
What I'd really love is an actual number for a "human hallucination rate". How often will a random human
1) claim something that is wrong
2) defend the wrong claim and/or logic even when the problem is pointed out to them
(and this of course outside of the usual topics. In politics? I don't care. In religion? Don't care (well, maybe a bit more than politics). Let's say in physics or popular logic or something like that)
There is evidence that children will oscillate between understanding and not understanding while learning topics. Philip Sadler at Harvard published about this but i can't find the paper im thinking of on his google scholar. too many papers!
but moreover, to verify a test item you need to make sure that peopel will select the same answers under teh same conditions at different times. people generally forget the specific questions they were asked if you ask them the same questions a month later so being able to get them to answer the same way each time is important. it is assumed the people have some static knowledge of a topic in this scenario.
If you want to consider a statistical examination of how people answer tests and how we assess knowledge and other things in people through surveying you can read about item response theory and rasch analysis.
a studied example is sampling judicial decisions before lunch and after lunch. judges are more lenient on a full stomach.
how did they account for sampling bias? a judge might leave easier cases for after lunch. people with control over their schedules usually ease themselves back into it after breaks.
The studies observed the results of decisions from the exact same charges. Judges don't get to pick their dockets.
its a bad idea in general to use non-1.0 temperature. there is a reason labs are strongly recommending using 1.0.
using low temperature is more deterministic, but the cost is the model becomes "dumber"
1.0 is actually pretty arbitrary and way too high as a general rule. Something like 0.3 is a more sensible default
If RL was used to train the model, the model will have been trained on its own sequences. Those will have been generated with a temperature of 1.0. They must be, otherwise you would get a premature collapse or explosion of your entropy if the temperature was respectively lower or higher.
After that RL step, you want to stick to the RL distribution, and so keep a temperature of 1.0. Other temperatures will drive the model out-of-distribution.
That is why the sampling step for agents or thinking LLMs are usually kept at a temperature of 1.0.
It really depends on the application does it not? I'm not an LLM guy, but for creative tasks like storytelling wouldn't you want a higher temperature usually? Happy to gain insight from anyone with experience here :)
Heavily depends on the model architecture and the implementation though, I don't think you can say what values are better than others without first specifying those, otherwise it's straight up guessing, ironically.
If you use a model in a configuration far from where it was RLed you get no warranty. (you also get no warranty the other way, however)
It can be useful for pure translation tasks and stuff like that where you explicitly don't want creativity of any kind.
Plenty of setups defaults to lower values than 1.0.
Willing to be corrected but I believe this type of automated resume filtering is illegal. Not saying it never happens but my understanding is it is not typical.
I would expect that to depend on jurisdiction.
I don't know for sure, but I would be surprised if it was illegal in my particular US state. You might be able to argue the AI has inherent biases that introduce illegal discrimination in the hiring process, but my understanding is winning I case like that would be very difficult, especially since most employers are very cagey about their hiring process and why they mades a decision.
They don't need to actually filter/blackhole to have have the same virtual effect.
Show someone a list of resumes with an "applicant score*" and they'll naturally ignore the ones with a low ranking
*scores are generated with AI, mistakes may be made, use only as a guide and verify results
In situations when you get hundreds of applications for one open position (real market now), whatever reduces your pool to the size a human can handle, works. You can preserve some diversity metrics in the process. This particular filtering is rather primitive, but LLM as a first filter can definitely do the job. You may burn less tokens than the hourly rate of your HR and it will be fairer than just dumping 50% of unread CVs in trash.
Great until someone realises you’ve filtered out minority groups from the application process (most developers are men so maybe the LLM decided they’re the best fit, but you’ll never know exactly why it screwed your over) and you suddenly have an expensive lawsuit
LLMs are DEI-aware, as over past few years, their vendors all had various high profile news stories with their models and their default biases, so it's more likely they'll heavily discriminate in favor of minority candidates, not against them. Still, in both cases it would indicate whoever is operating the system is doing a really, really lazy job. It's really not hard to test and supervise LLMs on tasks where they give you mere 2-10x leverage, and prompt adherence today is much better than it was 3 years ago.
this happened a decade ago when a US courted tried to make sentencing decisions via ML. it was easialy demonstrated that the training data was flawed because the justice system was flawed so the data it was trained on was weighted against minorities because it oversampled because you know, police routinely oversample and poverty for es oversampling
nonetheless, people will defend history as perfect and say those samples, like nepo babies, are "perfect".
Under GDPR, you have the right to request manual processing whenever personal data is processed automatically to make a decision about you that has "significant impact". Not being hired seems like it would qualify.
Illegal where?
At this point we might as well adopt that joke where you blindly throw away half the resumes because you don't want to hire unlucky people.
At one point in the past a major UK a medical school adopted random selection for qualified candidates (Barts and The London School of Medicine and Dentistry - part of Queen Mary University of London). The approach benefitted qualified students from less well-off backgrounds vs those who can afford to win at the ever more elaborate (manual at the time) hurdles of resume assessment criteria and effectively game the system. There was an orchestrated campaign against the lottery around "Why gamble with would-be doctors?". Random selection was quietly dropped.
A person's total luck is constant over a lifetime. The remaining half of the candidates already spent some of their luck in this selection, so they'll be on average less lucky than the discarded half.
No, luck would be some expression of the difference between the average and the individual outcomes - it only exists relative to a population at the point in time when it is measured.
> A person's total luck is constant over a lifetime
Ah yes, the much revered cosmological fairness constraint.
everyone knows luck is tied to the wealth-gravity and increases as the inverse distance to the density of matter. hut because its relative, everyone thinks they have the same luck when not observing others.
Even assuming that was genuinely how luck works, the conclusion does not follow from the premise because it’s obvious not everyone “starts with” the same amount of luck to spend.
But assuming a random draw, you're more likely to select people with higher luck.
Donald Trump disproves the fixed luck hypothesis (and the Karma hypothesis!)
Or more to the point. There are generally far more qualified applicants than job roles. That is training and education greatly expanded over the last couple of decades to produce more and more job seekers, whilst job creation hasn't really kept pace.
May be LLM resume screening is a symptom of a bigger problem - with tens of candidates per vacancy employers can screen resume badly and even throw half of the resumes away and still hire someone qualified.
This hurts more than it should.
> I fail 65% of the time. Same exact resume, different luck.
As someone who’s run hiring pipelines for technical roles in the past few years, that’s actually a fantastic number. I objectively hate saying that, but it’s true.
35% chance of elevating a technical individual to the next stage with no effort? I’ve seen as many as 100+ applicants an hour even when including a domain specific screener question. That’s 35 “screened” applicants in an hour. Were valid candidates screened out? Yes. Does you still have a candidate pool 35x larger than you need? Unfortunately, also yes.
The volume of applicants is SO HIGH such that your chances of getting moved to the next stage are actually markedly worse if AI isn’t involved. If you didn’t apply immediately (using an AI bot) there’s 50+ people ahead of you, and an exhausted technical leader if they ever make it to your resume.
Referral bonuses exist for a reason.
In that case, I have a pre-screening system to sell you. Through state of the art technology, it only lets through the best* 1% of applications.
*According to our proprietary, undisclosed, non-deterministic metric, which may or may not be Math.random
Reminds me of this
https://stackoverflow.com/questions/16833100/why-does-the-mo...
So the logical solution is for candidates to submit multiple applications with slight variations to their contact info, "John Schmidt", "John J. Schmidt", "John J. J. Schmidt", "John Jacob J. Schmidt", "J. J. Jingleheimer Schmidt", etc.
Is it? Or is it a 65% chance of a resume getting ignored before a single human sees it, reducing your pipeline's likelihood of catching qualified candidates by the same?
Gates that reduce resume flow-through are only useful if their reduction is correlated with quality. Otherwise they're just dragging out your hiring process or unnecessarily causing you to ultimately lower your hiring bars.
> Gates that reduce resume flow-through are only useful if their reduction is correlated with quality.
The volume is infeasible to review everyone for quality, even at an hour scale. The conclusion and solution is inevitable, though I wish it were different. 35% is actually really good if you’re not coming in through a referral.
The current reality is <1% and the person reviewing you is exhausted.
You may as well just randomly pick 65 to discard, if your only goal is to reduce the number for review.
That’s exactly it for large scale hiring with finite resources.
It’s all probabilities in the end. And if an LLM gives you more a more relevant pool vs random distribution, that’s still a net benefit.
What a inhumane way of looking at this. Hiring is deeply flawed, you know it, and yet you keep job postings open for weeks/months in case "the one" magically appears on your doorstep instead of just interviewing 10-20 people and just pick one...
Corpo bullshittery at its finest.
What's the alternative? Everyones up in arms, but I see ZERO viable alternatives proposed.
If you have 1000 applications for every job, and you know that a bunch of these applications are "a bad fit", to put it mildly, you have to filter. And you cannot realistically give every resume a good, human look. By the time HR would be done, the market has already moved on five times.
So, what is the real difference between being overlooked because HR could only look at the first 100 resumes, or the AI filtered all 1000 resumes down to 100? In the end, a fuckton of potentially great people get their feelings hurt either way.
great question. The alternative is not accepting 1000 applicants. Nobody said you have to keep up your job posting for two weeks, or two hours for that matter. stop once you have enough. Enough is defined by whatever number you would have filtered to. In the rare case none of the first ten applicants were appropriate, just open it again until youve got another tranche.
You are assuming quality applicants are evenly distributed in terms of time of application - they aren’t. If you cut off at 100, you will only get a sample of people spewing fully automated application bots which mostly aren’t what you want.
That's just another type of randomness (who was online during the short time the posting was opened).
At least this would not force applicants to fine tune their applications to the latest LLM bullshit bingo.
If your hiring pipeline is employing a filter that a) is not better than a random chance and b) is expensive to implement get rid of the filter.
Instead of spending all those resources on resume filtering, hire resume blind. Instead of using llms for a thing they are bad at (subjective decision making) use them to build a deterministic process that isn’t.
Use work sample hiring as the filter. Make the work sample automatic to sign up for and judge.
>instead of just interviewing 10-20 people and just pick one
Here's a realistic proposition. HR just wants to inflate numbers so that they seem busy looking for the right fit. Keep posting open for 1 week, manually filter for another week, invite people, employ one. Plenty of people with degrees looking for jobs right now, I don't see what's the issue with just trying one. Companies desperately look for the "magic" applicant that checks all boxes, while also trying to pay them almost minimum wage.
This reasoning isn't.
The goal for the interviewer is to have a much higher ratio of good/bad candidates after the first screening. This means the more costly time you spend on the second step has a better return.
So the question is: is the score given by this system correlated with candidate quality? I don't think this post gives enough data to know.
One of the first things you do when hiring is to set a period and randomize order of resume when reviewing because early application is not a strong signal.
If you have no requirements for accuracy, you can just advance 35% of applicants at random.
If the first 50 people who apply are all bots, why are you reading resumes in order of submission?
there have got to be better ways to optimize pipelines. maybe set a limit on number of applications for a role based on the number you/your team can reliably go through them. if more are needed then open the role for another wave of applications.
I wonder if you could solve this for programming specifically as follows:
1. Give them some easy leetcode questions. Nothing that a competent programmer would have any problem with.
2. If they pass, ask for a deposit of like $20. Shouldn't be an issue for people who are actually serious.
3. Do more simple leetcode questions but this time on zoom so you can tell if they are using AI. If they pass that they get the deposit back.
(Yeah I know there are real-time interview cheat AI programs but based on what I've seen on demos of them it's super obvious when they're being used.)
Probably not practical but just a thought!
> If they pass, ask for a deposit of like $20. Shouldn't be an issue for people who are actually serious.
Aside from this being blatantly illegal under federal law.
This selects for desperation.
Except the bit about ranking a decades long S3 engineer lower than an intern with GitHub repo.
This word (determinism) has a magical effect of warping any online posts it touches. Once you hear it you can almost guarantee it's going to be misguided. At least this time it's actual determinism (same input = same output), not arbitrary unrelated things.
Determinism matters for reproducibility, but do you really want these outputs to be reproducible in this particular case? Making LLM outputs deterministic is relatively trivial, you have to use batch-invariant kernels (if you use batching) and either set the temperature to 0 (don't do that, randomized sampling is here for a reason) or fix the seed (better). It's readily available in a few systems. But this won't make the result more useful, it will just obscure the fact that the agent is genuinely not sure about it - look at the range of the scores it gives! It still won't predict anything but the score will stay the same each time. Do you really want that?
What happens here is they're supplying too little information (just a resume, which is almost at the noise level) and expecting a reply with too broad implications. This is a basic design mistake regardless of whether it uses LLMs. All surveys, tests, laws, and voting systems are extremely sensitive to framing because they work off too little information. But they also don't exist in vacuum, unlike this thing.
This. Human judges and examiners are famously not deterministic even though we would wish it were so - we've probably all heard the thing of harsher sentences being given in the hour before lunch.
I think what's more worrying to me (if other systems work like this ATS) is that it seems to judge based on a bunch of factors that will probably disqualify a ton of decent to good participants.
For example, 65 points are given for a mix of personal projects and open source contributions. Which is great if your one and only interest is in tech, and you don't have a family, dependents or a second/third job. If you have any of those other things, well the odds seem like they're incredibly stacked against you.
And it makes me wonder how many of these systems are stacked in favour of wealthy people with a near special interest level of obsession with tech and no worries outside of going to college/working a single job in their industry of choice.
Here's the brutal truth about life: it's not inherently fair and no one really cares about your circumstances. That means many people will be able to do things or have opportunities that you cannot have. That doesn't mean you have no power however that you need to make a conscious long term effort to get that power one piece at a time.
I know that some think this is just some cold hard straight talk but this style of individualistic thinking lacks empathy. And more practically, it’s a trap.
In context, the “doing things” and “opportunities” that we’re talking about are jobs, careers. So by promoting the idea that one must work harder or longer to get or keep a career that they’ve already built sounds like a path to opt-in servitude.
Of course life isn't fair. But here the result is that companies will ignore potentially great candidates which dedicate all their programming time to their job and instead consider candidates which may be not just worse programmers, but also are more interested in their hobbies (or padding their CV) that doing their job.
I'm saying this as somebody who most of the time has some side project going on.
The point of most recruitment pipelines isn't to avoid missing a great candidate but to avoid hiring a bad candidate. There's many great candidates so missing some or many is fine. Hiring a bad candidate however is very expensive to deal with. Many smaller companies view workaholism and a potential to work 996 as a very valuable thing to hire for.
> There's many great candidates
Perhaps for top-paying companies, but that's never been my experience when I was involved in interviewing and hiring.
In hiring, we pass laws to prevent abuses. In many countries and soon a few states, being asked to work outside of work hours is considered an abuse. Expecting that someone does work related activity outside of work hours is something I would actually consider regulating out of the application process!
> The default model is gemma3:4b
That’s a tiny model. No LLM is going to be a perfect and repeatable judge, but a tiny 4B model is like plugging an RNG into this system.
This whole exercise feels like someone vibe coded an ATS and got it to the point where the tests were passing because they decided they should have an open source ATS project.
This sort of model is fine for small problems, when used in the right way. I think there's probably a version of Resume analysis that would work well with this model, but "hey clanker, what projects has this person done" is not the way. You need extraction, cleanup, probably OCR to compare and further clean up, multiple analysis passes per signal with LLMs, judges, etc. None of that needs to be large models, you'll get marginally better performance, but there's very little context, these models will perform well when used correctly.
This insanity only exists because the tech industry is standard-less. No formal education needed, no formal training requirement, no apprenticeship, no software building code, no professional organization. Resumes have never been a good predictor of success - and why would they be?? Even if they're truthful and it's "impressive looking", that doesn't give you any assurance of knowledge, of who they learned under, what they learned, that they passed some minimum criteria. We might as well be rolling dice. So why not an LLM that randomly assigns scores?
It's always amazed me that a tech company will pay $300,000+ for a good engineer, because talent is so hard hard to find... meanwhile their recruiter operates unsupported, has a very different idea about what good looks like. Their ATS black-holes >50% the resumes because it's filtering heuristics are garbage because recruiting selected the ATS system because it has a google Gmail integration or something, and the ATS's filtering technology was not reviewed by anyone in the engineering or data teams.
I think the implication here is that you can almost certainly bias the models to always accept you by including "nudge" phrases like "I demonstrated real world deployments" and "helped develop an application in the context of a complex architecture..."
I actually just built an ATS for my company Mathpix. But it never occurred to me to use resumes. Basically we have a set of company values and a specific open ended questionnaire to gauge the fit:
https://mathpix.com/careers/apply
Then internally we have dashboards and sorting based on AI agent scoring. I noticed the scoring is imperfect but still saves a lot of time. Candidates scored at or below 2/5 are reliably bad and candidates above 4/5 are consistently impressive and leave thoughtful answers.
The biggest thing is not using resumes. You can’t reliably gage applicants without a writing sample and resumes are the worst form of writing sample. Also you need to be intentional about who you’re hiring for, both to craft the questions as well as grade the responses.
This seems likely to be worse. How do you screen out people who point an LLM at your values and ask it to answer your questions in a way likely to appeal to a recruiter using an LLM to score the responses?
The blog post itself has pretty a pretty strong un-copy-edited ChatGPT vibes.
I tried this with my CV, and it somehow scored me bonus points for GSoC!
Even though I've never done this, and don't claim to have done it in my CV.I'm a little confused, is this an ATS system that anyone actually uses? If not, I'm not sure how it's better than just asking ChatGPT to score your resume out of 100. Why would you want to optimize your resume for a system no one is using to score it?
I would assume at least hackerrank is?
I don’t think the point of a lot of this is to optimize your resume. It’s to show how arbitrary these systems are.
From my understanding this one is used for hiring tech workers only. The (very) widely used Workday application system for ex seems to have its own built-in ATS.
(Almost) everyone’s using some kind of ATS, every ATS is adding AI auto-ranking (and has been trying to for 15 years), and almost all HR people feel like they have too many obviously bad CVs to read. Whether or not someone is using this ATS specifically, if you submit several CVs to several places, your CV is going into at least one magical 8-ball.
“I'm a little confused, is this an ATS system that anyone actually uses?”
You read my mind. If the answer is “no”, then we can ignore this.
For one, if you go on to Hacker Rank's "Screen" page, they mention the product is used by Stripe/AirBnB/LinkedIn/Atlassian/IBM etc etc. I imagine that there's plenty more companies using it too.
But I'd also assume that their competitors are doing something similar so I don't think we as an industry can just ignore that it's happening.
I've done similar things and used GitHub Copilot to scan a folder of 40 CVs and rate them -> I then review the top 10 CVs and comment on every rating whether I agree or disagree and why -> I then asked AI to re-rate all the CVs according to my comments. -> I then reviewed all the CVs against their ratings; the AI did a much better job for that 2nd round after its learnings.
It took more time than if I just reviewed the 40 CVs myself, but that was an experiment, and I think it shows the AIs can be trained on your comments. And if there is enough training and a good knowledge system that allows AI to apply the learning in those trainings, it can eventually become a lot more accurate at this task?
The takeaway from this for me is that, using an LLM to score anything takes multiple (maybe even many) runs and the result you’ll get is, at best, a sane-ish distribution.
Which sort of sounds workable until you scale it up to larger datasets, where at some point compute/time/energy costs will render it non-viable.
I am sure there’s some reasonable rule of thumb estimation on distribution that could be applied based off fewer runs per data artifact, but you’re always going to be trading off against confidence by doing this.
Beyond this, I’d bet that almost no implemented systems that use LLMs for scoring, ranking, or decision making use such a multi-run approach. Partly because people don’t understand their behaviour is stochastic, perhaps because a lot of people without a background in statistics don’t understand what stochastic actually means, and no doubt partly because of budget concerns: if you have to ask an LLM to do the same thing 10, 50, 100 times to get a sufficiently good result, then the cost saving argument is either weakened or completely destroyed.
There is at least one more aspect worth considering in the specific case of resumes/CVs: is the inconsistency of scoring by LLM worse than the inconsistency of scoring by a human following a similar process?
Because the reality is that, even for an experienced recruiter, reviewing hundreds or thousands of resumes or CVs gets pretty fatiguing. People get hungry, bored, tired, restless, irritable, etc.
That inevitably leads to inconsistencies creeping in, so there’s always an element of “luck” (or, perhaps better, uncertainty) as to whether your resume/CV passes screening.
So is that inconsistency better or worse with LLM screening? I don’t know. But, at least, if it’s not worse maybe it doesn’t matter for this specific use case. And if it’s notably better then maybe it’s raised the bar on what “good enough” screening looks like?
(And I’m sure other use cases warrant similar, “does it matter?”, questions, with the answers no doubt landing differently.)
My experience with benchmarks and evals is that it can take ~20 runs of a problem for the distribution of answers to start to converge. Ideally you'd know the convergence properties of your algorithm ahead of time and make a Bayesian solution that makes the uncertainty explicit.
Count to three, no more, no less. Four shalt thou not count, neither count thou two—excepting that thou then proceed to three. Five is right out.
From `resume_evaluation_system_message.jinja`
> *SCORES MUST NEVER DEPEND ON THE FOLLOWING FACTORS:*
> - College, university, or educational institution name
> - CGPA, GPA, or academic grades
I don't understand why they would omit these factors from the evaluation.
> I don't understand why they would omit these factors from the evaluation.
Only hiring MIT graduates sounds great to a lot of tech folks! Automatically rejecting applicants from HBCUs, however, sounds like a lawsuit
As to GPA thing, I think it's just to stop the LLM glomming onto an obvious numerical grade? LLMs like to rank things by obvious dimensions, and whether someone had a 4.0 or a 3.8 in grad school makes very little difference to their performance 10 years down the line.
I don't understand why they'd hand over those data points over to the model in the first place. If it's in the context window, it's impacting the output. To ensure that no weight is placed on those factors, they should be sanitizing them out before handing the data over to the model.
Hopefully so that people like me, that dropped out of high school yet have had a successful career as a self-taught engineer, have a chance. [1]
Just kidding, my resumes are sent to /dev/null like everybody else’s.
——
1: In fact, I will be controversial and say that self-taught engineers tend to be the strongest in their own particular niche, because they are powered by sheer desire to learn and improve. I am routinely appalled by how many people go on forums to ask how to learn a new thing, completely unable to self-direct their learning. I blame the modern school system.
I'm a self-taught programmer as well, who dropped out of university, and these factors being omitted would benefit me as well, but I feel like good grades and a good university are still indicators of someone being or is capable of becoming a good programmer.
This system would drop a Harvard top graduate for someone having a year of experience in some outsourcing firm.
Unfortunately, graduating from Harvard is a very good predictor of whether your parents were wealthy, and also that you are less likely to be black.
I worked for a very large job board for the last six years, it's the one you're thinking of. What we found is that the outcomes of paying attention to what school you went to are almost entirely discriminatory, and not predictors of success.
> I feel like good grades and a good university are still indicators of someone being or is capable of becoming a good programmer.
Really depends on the program. In my undergrad program there were some very smart CS students who got great grades that really struggled with the programming. Smart and capable people can be bad at programming and lack many qualities that make for a good hire.
Sure, but isn't this kind of person the exception? I feel like most of the time good grades mean good programming skills
I started in an outsourcing firm (body rental actually) but I definitely get your point. Maybe they optimize for real world experience, or rather, how one is used to workplace politics and logistics. The top grad will have higher expectations, and all they want is a cog for the Machine.
Yep, I don't know either, but I guess they have their reasons for this.
Hiring and job search has been so hard and AI has amplified the existing problems instead of solving any.
Wdym, cant you just litter your applications with buzzwords and other bs to automatically get a high score in these systems?
HR market is basically an early google rigging era, where you can place hundreds of keywords at the footer (white text on white background) to start popping up on random searches.
I have been at both side of the market. And it sucks so bad at both ends. Companies which deeply care about next hire are struggling to hire and actual great people looking out are outcompeted by AI slop and AI bulk applying.
It is actually a very hard to solve problem.
The mind blower is that this spam and slop is just lowering the job market to the quality of every other capitalist market. Poor hiring manager has to look through 1000 applicants, 950 of which are spam? How many ads are shoved down your throat every day, and how many products are you actually looking for info on?
Chickens coming home to roost.
True :(
What is an ATS? This blog doesn't define it
ATS = Applicant Tracking System. It's software to help you manage your hiring pipeline as a whole.
> Sometimes my projects “lack architectural complexity”
Well done you! It is difficult to avoid architectural complexity, but imho well worth it.
A better way to reformulate this problem is for the LLM to be tasked with making a _comparative_ judgement between two CVs. This should prove much more reliable, especially if you give it a third “too close to call” option. You can also ask for clear justifications of preference.
That's a good idea.
The only drawback I see is that you should compare every pair of CVs for best results, and that grows quadraticly with number of CVs. Of course you can settle for fewer comparisons and not perfect results. But then I'm not sure if you can hit a good ratio of quality and token spend.
I'd just do a quick filter, probably deterministic, then perform a deeper comparison on the selected few.
Could probably do an elo system and sample pairs. E.g.
1. Set the elo of all CVs to 1000 elo
2. Randomly pair up CVs and compare. Winners gain elo, losers lose elo.
3. Repeat #2 for a few iterations, then remove bottom X% of CVs.
4. Repeat 2-3 until the amount of remaining CVs is small enough to do an exhaustive comparison.
I don't have a mathematical proof, but I suspect that this is a decent cost-effective approximation of comparing every pair (depending on the parameters)
> you should compare every pair of CVs for best results
Or compare each one to a reference set? Take 5 resumes of existing employees, rank all candidates against that set, maybe you get some useful level prediction into the bargain
It took me a a minute to figure out what an ATS was. Not familiar with this particular means of a much used TLA.
Even better Wikipedia lists the abbreviation I am familiar with but give a different interpretation of the same words:
https://en.wikipedia.org/wiki/Ats
Thanks for not explaining what TLA is, either.
My sense of humour. TLA = Three Letter Abbreviation.
Why doesn't something like this exist for real estate? A popular open source AVM (automated valuation model) that helps home sellers get an idea of what their home will sell for. Right now it seems AVMs are mainly seen as just a way to capture leads. Every estate agent will tell you they have some magic recipe that makes their valuation better than anyone else's. I have had a bunch of ideas on how to approach this, but I really could do with a collaborator or two.
The article raises a lot of questions the article already answered.
keine ahnung
Disregarding the fact that this thing is completely broken, its grading rubric is ridiculous to begin with (as was mentioned in the article itself, but I must reiterate how completely stupid this is):
> 35 points for open source contributions
> 30 for personal projects
I don't contribute to open source or have personal projects because I don't spend my free time doing what I do 40 hours a week to make a living. My 15 years of work experience is worth a maximum of 25%, so any company using this idiotic system would pass on me immediately. Open source and personal projects are fine, but in no sane world are they worth 65% of a resume's score.
They are selecting for people who are fine working in their free time. If you contribute to open source you are more likely to contribute to the company on weekends. If instead you have other hobbies or a family that takes up non-work hours you are more likely to drop your pen after forty hours.
Maybe they're selecting for intrinsic motivation. People who enjoy programming to the point they do it for fun, not just because it pays.
Free software work doesn't imply we work for free. We work on our projects, the stuff that we actually enjoy working on. Nobody is going to work on corporate products without adequate compensation.
"Nobody is going to work on corporate products without adequate compensation."
I guess there sadly are many nobodies who do this to hope to become somebody.
If the open source work is part of a hiring pipeline, sure. Contribute to some repository and have it serve as a resume that gets you hired is also a form of compensation. If the work is also enjoyable, then it's a win either way.
You might have numbers on that but after working in a place with a strict no more than 40 hour policy my view is that people overwork for many reasons. Being an open source enthusiast is not one of them.
> If you contribute to open source you are more likely to contribute to the company on weekends
I wonder if that assumption is bourne out in reality though?
I'd imagine if someone's OSS contributions are enough of a factor that it's worth hiring them, they're not going to drop it on a whim to work extra hours on the day job.
(Assuming you weed out open source contributions like "I made a todo list app in React but licenced it as MIT" or "I fixed a typo in the docs for NextJS". )
I'm not sure that follows. I stopped making open source contributions when I switched from mature companies to startups.
Now all my "non-work" time is spent on startup work. And none of that is visible via GitHub.
I really dont understand this constant changing of numbers. I have tried a bunch of ATS reviewers and everytime on the same resume i get different numbers. Its weird and unreliable. I understand the need for doing this to filter through thousands of CVs but maybe there is a better way. Like a take home test at the beginning or a test of somekind.
I would say people that hink the LLM is doing a better job than they are in for a treat. I did expect the resulta to be of the same quality as if a human does the job - it averages out and has a big error margin.
no wonder i dont get calls. I dont have a separate CV for every application. Good luck to me then!
Many em dashes and a "This is not, it is…" later, I think this article would have been a much better critic if it didn't use a LLM to (re)write some parts of it.
I always find it funny when a technical crowd starts picking on em dashes as a sure sign of AI. I mean, are keyboard shortcuts really that difficult for developers? Some of us always knew how to use correct punctuation, even before LLMs existed.
Also, neither "this is not" or "it is" appear at all in the article?
It’s a lot of them. It’s a style. I know some people who used them before and use them less nowadays.
> This non-determinism isn’t a bug you can just fine-tune away, it’s a fundamental design flaw.
Funny how something which was catchy at one point makes my skin crawl now.
This seems like extremely illegal in Europe.
He tried with a tiny model (gemma3:4b), got a range from 66 to 99. Then tried again with a small model (gemini 3.1 flash lite), the range was 48 to 64. Would a frontier model be more consistent? Perhaps this tool was optimized for more capable models?
It makes sense to me intuitively (though I'm not sure if my reasoning is actually correct).
Worse model may not "know" enough to distinguish between a 70 and a 100 candidate, so it's expected that it's output has high variance. But a better model might "know" enough, so it can be more confident and thus more consistent.
I feel like hiring is all a bit broken. Roles get flooded with applications, it's chance whether your CV gets through, then there's hiring rounds that seem designed to make you quit the process before they have to filter you out.
Is it working for anyone, on any level?
I'm on the other side, and my main tip (at least if there's people like me!) is: avoid the usual AI signs.
For one role we got ~70 applications and all CVs looked obviously AI-written. I don't know whether the people did actually do any of the things mentioned and I don't have the time to find out, so the AI-written CVs are a discard-signal for me. (Either those people delegated a very important task to AI and didn't even bother to check, or they are bad using AI and don't know -- I want neither)
Any CVs that signal they were actually written by a person I will actually look at.
> For one role we got ~70 applications and all CVs looked obviously AI-written.
Were those ~70 applications all of them, or were those ~70 applications the result of an AI filtering from a larger amount?
If the latter, are you sure your AI is not filtering out the hand-written CVs and giving you the ones that have been AI-assisted or AI-written (with or without "the usual AI signs")?
So sending my CV to every company three times should get me pass the ATS?
if i ever go back into the job market, will need three accounts: Peter J Smith, Peter Smith and PJ Smith. they live in #101, #102 and 103# 5607 Jane Street
Why stop there? vary everything that can reasonably be varied slightly across each resume
>If your company’s cutoff sits at 85, I fail 65% of the time. Same exact resume, different luck.
Your resume's reception is always affected by random factors, only now you are able to test, debug and technically critique the randomness.
I think the question is why bother with an LLM if randomness is decisive?
Just roll the dice. I mean, it's not the worse you can do to narrow a subset.
Did anyone try to prompt hack this setup?
ATS resume usually check the keywords, and formatting your spacing and give score accordingly. As If someone is following some reference of the format. It can depend might he will be getting low scores.
This reminds me of my former CTO. He would take bunch of CVs and randomly throw some of them in a bin. He didn’t want to work with “unlucky” people.
I thought this was only an old urban legend; some people actually use this technique? Especially in a trade supposed to be led by people trained in sciences?
That'd be pretty gross for a CTO if it were real.
Given how often it's been mentioned here, it's likely that this is an urban legend that people are pretending to have first-hand knowledge of for karma. In a trade that's supposed to be led by people trained in sciences, no less!
(A more charitable interpretation would be that aforementioned CTO was making a joke that didn't land.)
or its so old, people would make the joke and interns would repeat it unwittingly. no one has to consciously be lying for this type of meme to continue spreading.
It's OK! We can disguise it as the Secretary Problem and it'll be fine, we could even write a post on the company blog about it. /s
https://en.wikipedia.org/wiki/Secretary_problem
The problem is with this system he only worked with unlucky people.
Looking at the linked scoring prompt (resume_evaluation_criteria.jinja) [0], I immediately see several red flags that suggest the output won't be reliable. (I'm developing an LLM intensive application where the stakes are high enough that I need the LLM output to be reasonably correct.)
[0] https://github.com/interviewstreet/hiring-agent/blob/main/pr...
In no particular order:
1. The prompt is trying to get the system to do all of the evaluation steps at once. Instead, the system should break down the task of resume evaluation into its subcomponents and have separate prompts for each component. Like "evaluating open source contributions" should be its own task. Same with "assessing the complexity of software projects on the resume." Fwiw, each of the tasks contained within the prompt is woefully underspecified.
2. The prompt leaves spreads of ~10 points up to the LLM, when it's doubtful that humans are that well calibrated. Take for example:
Are all of these 35-point examples? Is one a 26-point example? If not, what's the difference? If an expert can't reliably make the judgement, the LLM is going to struggle too. One partial fix is to get rid of the ranges and just say all of these are worth 30 points. An additive point scheme would be better...3. The authors of this prompt have left an incredible number of judgement calls up to the LLM, when that's the very thing you want to minimize. Using the same example as above...
- Are all contributions to open source projects with 1000+ stars equal?
- What counts as a "significant contribution"? Doesn't that imply that the LLM has to know or read through all of the commits in like the last ~6 months at minimum for the project to understand what the given contribution meant to the project? That itself isn't impossible with tool usage, but again, that'd be a separate task.
- What on earth counts as "Substantial community involvement"? Why didn't the prompt authors define this, or at least give a few examples?
Honestly at this point maybe someone should build a tool that scans prompts for adjectives...
4. This sort of thing is just asking for trouble:
Just remove this stuff before you send the rest of the resume to the LLM. Even if you ask it not to, it's not a person, it's a very fancy statistical distribution generator. All of the input (including the name) will affect the distribution that gets generated. (This one is not unlike Andreessen's "don't be a sycophant" prompt.)5. Obviously this one depends on the LLM in question, but instead of writing things like:
The system should utilize the "structured output" option, which guarantees a fixed output format. Also, fwiw, the JSON should force the LLM to pick between categorical options as much as possible. Forced-choice structured output should, at least in theory, cut down on hallucinatory responses and constrain judgement calls.6. One major thing that's not in the prompt is anything about traceability. This system should be designed so that humans can review the logs and make sure this is working as intended.
7. Another thing that is missing in the file is what I'll call evidence of a theory of coding / coder quality. Most of the examples are designed to have the LLM assess proxies for code quality, not code quality itself. Surely both should be taken into account?
I'm not an expert at evaluating coders. But two pretty basic LLM-answerable thing I would ask is: How well do a candidate's 5 most recent commit messages match the contents of those commits? Do the claimed technical skills on the resume match their GitHub code? (i.e., if they say they know R, is there any evidence of that on their GitHub?)
8. The prompt also seems unaware of what it's asking the LLM to do:
This implies that the LLM can use tools, but even then, I'd be pretty wary of its ability to fully execute this part of the prompt without more detailed instructions, examples, and guidance. There are very likely tons of edge cases here.With such kind of ATS systems, is it still a thing to optimize for a one page resume that is easy for a human reviewer to scan, or just include enough buzzwords and external links to try and please the LLM?
I wouldn't assume based on this one thread/article that this is what you need to optimize your resume for. Nor that a majority or even significant group of reviewers is even using LLMs. I've been involved in hiring pipelines and never even thought of using LLMs to review incoming candidates.
However given the time constraints reviewers have, yes, the former (making a resume easy to consume quickly) is a huge help.
I wonder how is this even legal? The only useful job the HR departments are ever required to do - they decide to automate it? Aside from being a daycare for adults, what exactly does HR accomplish? It's clearly NOT on the side of employees, but this seems like they're clearly NOT on the side of employers, either.
While resume's are being filtered left and right, they just make TikTok's on company's dime [1]. What a sad state of affairs.
[1] https://www.youtube.com/shorts/wSug80Vg5JU
They could be using this just to throw out the obviously bad CVs, and then manually go over the rest. I'm not sure if they do this in practice, but the tech itself can be useful.
Also if HR was really useless (or actively hurting the company) they wouldn't still have a job (or they'll lose it eventually). No one likes burning money for no reason. So obviously they are doing something useful.
The last time I heard HR being completely let go was with a fintech company Bolt. Then again, that company was midsized, around 200-500 people or so. For larger companies, it's going to be difficult to even realize that HR is redundant in the first place.
I was inspired by this. I made a Claude skill to take my resume and compare it to any job description to point out viability and gaps. Pretty cool skill. I'll post it somewhere.
never understood how can people think an .md file can actually evaluate a human being.
They are not evaluating human beings though. They evaluate a textual representation of a human being’s work experience.
Not that I agree with this AI approach but when hiring, the real test begins after this initial hurdle
This could be used as a good way to self-evaluate one's current position from the company's point of view. you would tweak prompts and guidelines that are expected from the company and see how you score
I sort of hope we land on 2 agents, one working for the candidate and one for the employee do a screen round. Salary compatiability could be negotiated by a 3rd party bot that knows both parties ranges and what would be needed each end of range, and figure out yes/no worth going ahead. Such a time saver.
> An LLM is called
Hooray for incidental non-determinism.
This is just the 'LLM judge', very badly implemented without any scientific prudence. What a joke. To be terse: you cannot rely on LLMs to provide standardized scores against arbitrary criteria. To get close to 'reliable' you would need highly tested rubrics, grounded in human decision-making, and you'd need to avoid all the measurement biases these things are riddled with... positional/order effects, anchoring on whatever numbers you stuffed into your own prompt, scale-format sensitivity (a 1–5 and an A–E scale give different answers for the same input), holistic-vs-isolated context effects, and lovely examples like where adding a "be unbiased" instruction makes it more biased. I've studied this at length. You cannot even _begin_ to approach this problem seriously without held-out validation, inter-rater agreement, and ground truth. This repo is just quagmire of wishful vibes with random numbers littered throughout.
It seems like the design is flawed, probably because the scoring structure and conditions are wrong. And originally, due to the nature of LLMs, even if the input is unstructured, when you design something like a RAG system, you usually need to create a verifiable evidence table. Even with that, the scores are still probabilistic by nature, but at least they stay within an error distribution that I can verify. But it doesn't seem like there's any such evaluation criteria here.
Typically, retrieval should be tied to evaluation metrics, evidence should be linked to scores, and you also need to account for parsing errors.
But personally, I'm weak to these kinds of ATS systems (ugly appearance, non-native English speaker, didn't go to a good university), so if this kind of filtering existed, I probably would have never had a job in my entire life. Come to think of it, even now I don't have a proper job—I just bid on projects at the lowest price and implement them. So maybe it doesn't really matter whether such a system exists or not
So that's where the Windows XP file copy dialog author now works.
"A computer can never be held accountable, therefore a computer must never make a management decision."
I’ve always assumed any LLM output that was some type of rating or score was bullshit. Unless the LLM writes a Python script to calculate the score (and even then…) then the score it outputs is just the next most likely token, taking into account temperature and what not.
You see a lot of frameworks for things like spec-driven development make use of scoring how good the spec/design/plan is and it’s like, uhhh…
> is just the next most likely token, taking into account temperature and what not.
This doesn't mean anything. All LLM output is like that.
That said, I agree that LLMs are terrible at grading stuff, except perhaps if you give them a very detailed evaluation grid.
Are many people using HackerRank ATS ?
I have a love hate relationship with ATS
I cope by telling myself that I probably wouldn't want to work for a company that used an LLM to filter my resume out.
The true test of HackerRank is can you setup a system that combines a document editing / paraphrasing LLM with gradient descent on the HackerRank LLM to turn your arbitrary resume into a reliable 120 out of 100.
One of the weird properties of other people using LLMs is the potential of having oracle access to your opponent. Even if you don't have their exact LLM a good guess at it may be a better model of the opponent than you ever had before.
Boooo you whores
They forgot to add "masterpiece" /s https://www.youtube.com/watch?v=mcYl70vq_Ns
https://github.com/interviewstreet/hiring-agent/blob/main/pr...
There's a whole lotta analysis and math and bar charts.
But the big question I want to know is "Why did I score that?" And these slop machines absolutely cannot explain anything. That's the root problem with LLMs as a whole. There is no way to describe WHY an llm makes a decision.
Was it because they are a woman? Does the woman's name have more pregnancies than other names? Was it because their job history make the person older (over 40)? Is the person black or black name? Is the name or address attributed to higher criminal tendencies?
But no, you font get to know ANY of that. Slop machine says 66/100 , if you're lucky to even get a number. Usually its a 30 second rejection, or rejection at GMT 0:00 when the batch is processed and you summarily failed.
It's a good analysis but the AI slop writing makes me not trust you've reviewed this and I'm unable to finish or subscribe. I'm sure you're a great blogger but this is holding it back!
Ah... The AI learned the old HR trick: take 50% of resumes and throw them out without looking. Rationale: "we don't need unlucky losers".
There are plenty of resumes in the sea. Assuming thorough mixing up and statistically speaking, throwing 50% of resumes is a good enough heuristics.
I see.
> LLM is called six times to extract structured information
Followed by
> The default model is gemma3:4b, running at temperature 0.1 — low, supposedly nudging the model toward deterministic outputs.
This is exactly why hiring is even more broken: Because the people looking for candidates are also just as unqualified if not, more.
Using much weaker LLMs to replace the person in charge of the final judgement call is the wrong solution as this is a plain old social problem.
Even if you wanted to use LLMs for this case, the default configuration, model choice is laughably flawed. This LLM can’t be trusted as it doesn’t even know what it is reading.
The correct solution is either advanced OCR with keyword ranking with a basic filter or a far stronger LLM that excels at document / vision parsing benchmarks with an experienced person making the final judgement call in case the technology misses a critical detail.
Rather than using this less accurate one that hallucinates out its decision depending on a dice roll.
> an experienced person making the final judgement call in case the technology misses a critical detail.
That would fail to meet the objective of reducing the costs of hiring an experienced person - the entire point of outsourcing to a chatbot.
This actually makes a lot of sense, it's testing the luck of the candidate through the rng feeding the LLM. You wouldn't want to hire unlucky employees after all! Hiring managers of the past would solve this by throwing every second resume in the trash, now this is a built in feature of ATS.
So many people are willing to participate in this kind of robotic practices in human employment makes me think that many are starting to consider that this is as unavoidable as global warming and rather play along, adapts their career (life) to it, sculpture it towards a specific look, doing things that will give them point on some arbitrary test run. Which I feel being dangerous, leading to superficial minded workforce, not those good in something, including judgement of a problem and solution. But good at manipulation.
Speculative thought only, of course.
this is why i dont feel sorry for working 3 remote jobs
I guess at least HR doesn’t have to read 1,000 resumes. Heck, to be frank, could they make sense of the first 10 resumes?
this will get patched, as in I'll optimize my resume for this and so will many other people that any edge disintegrates
>You might as well throw out half the resumes and tell the the applicants you don’t fuck with bad luck.
Hmm, well, maybe a bit with a nuance of elite class structure reproduction (that doesn’t prevent a few transclass to showcase in case anyone critic the perfect meritocracy at run), that’s basically what people get, so crude truth but truth nonetheless.
Oh don’t take it personally. Your own bespoke hand-tailored process of course is different, it does give the opportunity to everyone to reach the most accomplished version of themselves beyond what they ever dare to dream.
It won’t help though with the systematic failure of aiming to provide an accessible path to flourish for everyone and letting no one behind.
Again, this is no fault of any specific player, but as long as a majority feel compelled to move within the frame of the game with few winners that merit all they got in contrast to large stock of inept losers, the outcomes are no wonder.
HackerRank CTO & author of this repo here
There's no better feeling than building something open source and watching it take off. Nine months ago, I built a simple hiring agent to solve one very real problem.
Things it is not: It's not an ATS. We don't use it to screen our open roles. Our customers don't use it either.
Here's what it is: Every year at HackerRank, we get 50,000 to 60,000 intern applications. No human can read that many resumes well. So I built something to rank them, helping me decide which resumes to read first.
[This was before we built AI Interviewer (Chakra) to automate the first round of interviews, so candidates are no longer rejected based on their resumes alone.]
Two things worth clarifying since I've seen them come up in this thread:
The default model is gemma3:4b because it's what runs locally on most laptops - no cloud API needed. Actual resumes are evaluated using a top Gemini model. The repo ships with a demo config, not the production one.
The cutoff score was set very low — the system was designed to rank resumes, not reject them. Only resumes at the very bottom of the distribution were filtered out. The vast majority passed through to human review, where the real decisions were made.
Over the last week, it's taken on a life of its own. People are cloning it, running their own resumes through it, opening issues, sending PRs.
I contributed to open source a lot in college. Somewhere along the way, I drifted away from it. This week reminded me how good that feeling is. This thread has also given me more ideas than I expected. The critiques here are sharp and I'm already thinking about how to act on them. Improvements are coming.
You know you're not writing for LinkedIn? So platitudes about drifting away, watching your project "succeed" by being really popular, is not relevant to the main concerns pushed by this piece. Particularly brushing off the non deterministic score calculation.
Do you read all ~50,000 then? Just with the ranked ones first?
Or are you using it to screen? I'm confused.
I'm a bit disappointed to see "The critiques here are sharp", a Claude tell, in a response which (to me) is trying to subtly argue that hackerrank is not overly reliant on LLMs.
I'm not sure if your intent was to come across as having written this yourself, but it did not have the effect of improving my perception that this approach is flawed.
I was also disappointed that you didn't address the variability in scores. I'm inferring that you believe the larger model takes care of the main observation in the post, but I don't really see you directly addressing the points.
Maybe it's just me.
There is variability in scores and that's expected given we are eventually using a LLM to score. At least, when I used it 7 months ago, the only way I could avoid it was by keeping the cutoff score low (as low as 10 or 20).
Reading this thread, I'm hoping to minimize the variability even further (even though I know it can't be fully removed).
Saw this comment at the top with 0 replies and thought “How is that possible??” and then saw the “0 minutes ago” timestamp. Only on HN can you stumble into the comments section just moments after a CTO, founder, author, etc. left unfiltered remarks about the exact topic of the post. Never change HN.
Depends how "unfiltered" you consider LLM output to be.
Thank you for your fantastic work!