When I was looking for my next role after being laid off, I didn’t get much of a response with my human handmade resume despite my experience
Just for kicks, I asked ChatGPT to “Analyze my resume and give it a score for what percentage it was in” then I asked it to revise it to make it score as high as possible
I still tweaked and fact checked it but after I started sending that out, I got a much higher hit rate than before
But who knows, maybe the market changed, was a better time of year, etc
I still had to pass interviews and prove my worth. But it probably helped me get my foot in the door
Same thing happened to my wife as well. I helped her tailor her LinkedIn profile and resume with a lot of attention to detail: adding metrics, keywords, results, etc. Nevertheless, she never received any outreach recruiters and got very few application responses.
Then she asked ChatGPT 5.x for help. I was skeptical about the changes it recommended (and was skeptical at all about using AI for this given the homogeneification it tends to produce). But somehow it worked: next day a recruiter reached out, then another, then applications started moving forward, etc.
My guess is that, as LLMs are shoveled into every phase of the recruiting process, not having an LLM write your resume for you is now playing on hard mode. The LLMs reviewing resumes are downranking resumes and profiles that are not "speaking" the same language and activating the correct neurons, thus preventing you from moving forward. This contrasts with years ago when we had more humans in the loop and the pasteurised writing of GPT 3.5/4o would make you look less worthy. Again, just a theory, but...
Even if we take this to be true, I'm not sure that it really matters?
It's comparing two resumes with the same information and picking one of the two. That's obviously a situation that would never occur in actual hiring. This doesn't demonstrate anything at all that indicates that LLMs would incorrectly preference LLM-written resumes in the real world.
It'd be interesting to do the same thing but with two resumes that are almost identical. One is slightly better (an extra year of experience or a specific note of some skill that is relevant to the role), and the other slightly worse one is written by an LLM. If the reviewing LLM picks the worse one in that case, you're potentially establishing a bias that would matter. As it stands this experiment just seems contrived and pointless.
I'll copy what I wrote on LinkedIn (note: I read roughly 25 pages, which is half the paper, and read it quickly)[0]:
"If I read the paper correctly, they don’t actually show that LLMs prefer resumes they generate.
Their actual method seems to be taking a human written resume, deleting the executive summary, having an LLM rewrite the executive summary based on the rest of the resume and then having another LLM rate the executive summary without the rest of the resume.
That’s likely to massively overstate any real impact, if you can even rely on it capturing a real effect.
I really wonder if I read that correctly, because I can’t come up with a justification for that study design."
[0] I couldn't help but mildly copy-edit before pasting here.
There is some creativity in the rest of the CV, between what kind of experiences are included and how they are described. But that would be far harder to generate fairly.
In think choosing the summary is a fair design choice since it prevents the LLM from just... making up a perfect candidate.
"I'm a fullstack professor of software design with 90 years of experience expecting a junior internship position"
When classifying resumes it is better to use the LLM as a feature extractor, think of 10-20 features you base your decision on, and extract them by LLM. The LLM only needs to do lower level task of question answering. Then you fit a classical ML model (xgboost for example) on the extracted features, based on company triage data points. This way you don't rely on the biases in the model, you can decide what criteria to use and how to judge cases without retraining the LLM. The feature extractor is generic, and the actual triage model is a toy you can retrain in seconds on new data points. It is also much more explainable, you can see how features influence decisions.
We are without our consent introducing a party in between people. The models become the arbiters of who does and does not get a job. It feels problematic.
Plot twist: The AI companies did this on purpose, this is how they can tell us that AI is going to take all the jobs from humans. That makes no sense unless they did this intentionally!
And I feel the common response of: well just use the model that’s available. Ai is and will probably always be resource constrained and profit driven, that means we will eventually see a world where poor people have worse resumes than rich people and there really won’t be any way around it because the man in the middle has the final say
before it used to be HR, so you always had a party in between "actual" people. HR (mostly) never cared about the CV, they just look at a checklist and see if it matches.
Take a look at how things worked before (and still do): employers decide who get jobs based on a combination of personal biases, nepotism, and ulterior motives while applicants present distorted versions of themselves and network/pull strings to put the odds in their favor. That seems more problematic.
That's what people on both side have been doing for at least couple years already.
Recruiters scan resumes for the best match with LLMs, candidates use the same LLMs (there's only like 3 of them) to tweak their resume for better match. I don't know what research you need to see why that makes sense.
Intuitively this feels obvious. Content generated by the model will be shaped by its training, therefore when reading it back it will resonate with that same training and have a positive view as a result.
Human when preparing a CV: "Make my CV more professional"
LLM many days later presenting a report to HR: "This CV is really professional"
There's probably more to it than that of course.
But it justifies my personal policy of using a different LLM family for code review tasks than for code generation tasks. To avoid the "marking your own homework" problem.
I think resumes will eventually (or have already) become obsolete in tech. The SNR is so low, they offer very thin filtering value.
Even taking the tiny bits of the resume that are "hard signal", like GPA, certifications, prior roles, etc, it doesn't translate into their performance in the initial screening interview.
This is why what I think the industry sorely needs is examination consortia.
Rather than trying to guess capability from the name of the university they went to, leading tech companies creating standardized tests in various fields, and your test scores form your "resume", so that developers can just focus on improving their scores rather than wasting time on resume/application/repetitive-screening toil.
Eventually even a system like that can be gamed, similarly to how Leetcode-maxxing and the like sprung up in response to typical SV interview questions. Studying for the job becomes studying for the test becomes studying for the pre-test test.
Seems kinda obvious, given that most large recruiting firms/hr use algos to analyze resumes and AI written version likely do a better job at hitting keywords/structure algos/llms pick up on...
Does anyone know of any HR departments actually using LLMs for scoring, selection, extraction, classification or any real use cases? I'm curious to hear about it and how they are using it.
You'll find the same is true if you have two different LLMs first independently come up with a plan for an implementation, then ask each one of them to say which one of the two designs/plans are the best. They're much more likely to favor the plans generated from the same model, rather than from other models. I'm sure, internally, this somehow makes sense, but it's worth thinking about if you're doing the whole "ask N models for voting/rating N plans to find the best" charade.
That's why I let the LM write it's own AGENT.md or SAFESPOT.md because it "knows" best how to write it so it can resume next time without issues.
Is hits the same spot as that I would take other notes than anyone else and no one could follow them as easily than I do. Everyone leaves the "of course" parts out of the notes if it's for the own use.
I suspect this is more a function of the corporate sanitization of language within the models. When I have passed my resume through the models for refinement, it often sanitizes some of the more easy going or simpler wording. It expands the vocabulary, makes it more dense, and uses more corpo speak in the bullets and formatting.
Each model likely has its own biases in terms of what constitutes correct corporate speak, and it chooses the resumes that best fit this.
Ultimately, I suspect it's more a function of model saying "this grammer, syntax structure, and formatting is most aligned with what is correct corporate language, so flag as high quality".
Well yeah, LLMs generate resumes (and other text) that they judge as superior to alternative plausible texts. Why would that judgement change just because a different instance hasn't seen it before? To anthropomorphize it, it's like having a hiring manager write a resume, get amnesia, and then have to judge it among other resumes.
Seems like obvious thing. If LLM have some weights involved on what is good resume to write there is very likely correlation to what would be good resume to rate. And this is probably a even good thing, at least from model quality perspective. Model itself should rate highly whatever it produces. There should be correlation between output and review of same output.
This may lead to some interesting gamesmanship. For instance, if I am applying to a company, and I know they use a certain applicant tracking system, and I know that ATS uses a certain model provider for its filter, I should then use that model to write the version of my resume I send to the company.
I just guessed that and got Copilot to rewrite my profile on the internal HR system. I also got a job spec benchmarked higher by getting Copilot to write it with that exact aim given in the prompt
Reading only the abstract: LLMs prefer output of their own generation over humans or even other models.
This is a very good reason to avoid using model-generated data to train future models. We'd be deepening this bias by continuing to do that, essentially forcing society to reshape their output using LLMs to increase engagement. This feels like a form of enshittification that doesn't just touch one product but all of society.
The only test that has worked 100% of the time for me is to read the candidate's code. Two hours is enough to precisely estimate the candidate's qualities as a software developer. I never understood why companies waste time with tests and quizzes because since it is so easy for me it should be just as easy for other software developers too. Of course, a candidate may be a jerk or unfit for other reasons, but ranking them on a software developer hot-or-not scale is not very difficult.
disclaimer: Not a lawyer, but studying towards CIPP/E.
You'd make no friends doing it, but as I understand it, for those that have GDPR as a statutory right then under "[Article 22 - Automated individual decision-making, including profiling][0]" you can request to know if your CV was screened by AI and what (and this is key) "meaningful human interaction" led to that decision. Technically this falls under a data subject access request and so a response is mandatory (but who really is going to enforce that - ICO / <insert your data protection agency here> probably isn't). Companies can't just smash a button and claim meaningful interaction, it has to be, well, meaningful and smashing a "nope" button obviously isn't meaninful.
If it turns out that it was only AI that screened it you can request a human review. Do not hold your breath.
Again, you'd make no friends doing it, but sooner or later a test case will emerge to generate some case law around "AI said no" because employment, or lack of because AI says no, does have significant impact on a human.
This is extremely obvious to anyone whose read other papers. There's tons of papers showing LLMs prefer their own outputs. It's a big enough problem that LLM-as-judge has to be a different LLM from the LLM you are testing in papers.
> As artificial intelligence (AI) tools become widely adopted, large language models (LLMs) are increasingly involved ... [in] ... decision-making processes
Absolutely! I don’t think people are really considering the full effects of just letting ai be the middle man. I mean Sam Altman basically said this is what he wants Gwen he said intelligence is a commodity no?
I wonder if this extends to training models on new content as well. Are we creating a cyclical information-consumption and training situation in which models being trained are more likely to pick up on and reference content created by themselves or by other LLMs than by other humans?
If you are a candidate who wants to be hired, and your target employers use LLMs to filter resumes, then an LLM-generated resume that the employer LLM-powered resume filters favor is "better" — as in "more likely to get you the job".
Or in other words: LLM it is optimizing function which is generated by same LLM, think you have random variable y, where generator sin(x+r) and your optimizer trying to fit function sin(x+unkown1) + unknown2 ("unknown" function) - it is obvious that will find best fit.
In text generation, LLM language is full of very emphatic phrases. At a surface level it might sound stronger. But as a human reader, it's not necessarily better
Where I work, my boss decided to make an application that uses AI to score long text field entries to ensure required information is present.
The AI lacks the ability to extract nuance and implicit information, which means entires end up being long winded and repeatitive. For each requirement its looking for, it must be explicity expressed-- it's quite unnatural, and almost feels like solving a puzzle, to which the obvious solution is to write a comment, then give it and the AI feedback to a failing comment to AI, so it can generate the proper structure the rubric-AI is looking for.
LLMs are statistically driven, and I can only imagine having the AI rewrite the comment produces a result that's more statistically fitting to the model than if any given human were to write it. So, it might mean, yeah, LLMs are better at writing resumes that the LLM can successfully classify-- are they better for a human to consume? Who knows.
“Do you really believe no human is going to read your resume at some point in the process and notice the classic AI tells?”
Even here on HN many people don’t recognize AI tells that are obvious. Pretty much 100% of all articles posted on HN have been AI generated for months and months already and people don’t seem to care.
I have very little faith in humanity being able to deal with the chaos that LLMs are going to unleash on society.
Heck, most resumes are probably skimmed at best already.
When I’m hiring, a human recruiter (or the hiring manager) reads most resumes.
For us, there is some sorting by basic keyword analysis and we start near the top, but there is no proverbial black box that rejects candidates outright.
If candidates are ignored by humans, it’s not because AI rejected them, it’s because we are starting with candidates earlier in the list and might not make it to applicant 537.
Rather unlikely to be the case, supported by the original article itself here, since if your statement was to be the case they would find that the human generated resume is 100% less likely to be shortlisted.
Obviously it’s not 100% of all human resumes are going to be filtered out, but it’s quite damning that human resumes are more likely to be filtered out just because they didn’t LLM-ify it.
Companies are using AI / LLMs to pre-filter resumes. These AIs prefer their own slop resumes. Not just human vs LLMs, but Claude prefers Claude resumes over ChatGPT. Nothing good can come out of that, when resumes are pre-filtered like that.
Unless, of course, you’re not being serious and just trying to be edgy on HN.
When I was looking for my next role after being laid off, I didn’t get much of a response with my human handmade resume despite my experience
Just for kicks, I asked ChatGPT to “Analyze my resume and give it a score for what percentage it was in” then I asked it to revise it to make it score as high as possible
I still tweaked and fact checked it but after I started sending that out, I got a much higher hit rate than before
But who knows, maybe the market changed, was a better time of year, etc
I still had to pass interviews and prove my worth. But it probably helped me get my foot in the door
Then she asked ChatGPT 5.x for help. I was skeptical about the changes it recommended (and was skeptical at all about using AI for this given the homogeneification it tends to produce). But somehow it worked: next day a recruiter reached out, then another, then applications started moving forward, etc.
My guess is that, as LLMs are shoveled into every phase of the recruiting process, not having an LLM write your resume for you is now playing on hard mode. The LLMs reviewing resumes are downranking resumes and profiles that are not "speaking" the same language and activating the correct neurons, thus preventing you from moving forward. This contrasts with years ago when we had more humans in the loop and the pasteurised writing of GPT 3.5/4o would make you look less worthy. Again, just a theory, but...
It’s not lazy incompetence, it’s quietly getting the job done with 1% of the effort (that was a sarcastic pastiche, in case anyone was unsure).
It's comparing two resumes with the same information and picking one of the two. That's obviously a situation that would never occur in actual hiring. This doesn't demonstrate anything at all that indicates that LLMs would incorrectly preference LLM-written resumes in the real world.
It'd be interesting to do the same thing but with two resumes that are almost identical. One is slightly better (an extra year of experience or a specific note of some skill that is relevant to the role), and the other slightly worse one is written by an LLM. If the reviewing LLM picks the worse one in that case, you're potentially establishing a bias that would matter. As it stands this experiment just seems contrived and pointless.
"If I read the paper correctly, they don’t actually show that LLMs prefer resumes they generate.
Their actual method seems to be taking a human written resume, deleting the executive summary, having an LLM rewrite the executive summary based on the rest of the resume and then having another LLM rate the executive summary without the rest of the resume.
That’s likely to massively overstate any real impact, if you can even rely on it capturing a real effect.
I really wonder if I read that correctly, because I can’t come up with a justification for that study design."
[0] I couldn't help but mildly copy-edit before pasting here.
In think choosing the summary is a fair design choice since it prevents the LLM from just... making up a perfect candidate.
"I'm a fullstack professor of software design with 90 years of experience expecting a junior internship position"
largely factual? A resume is usually more than a bunch of dates and titles of positions.
Recruiters scan resumes for the best match with LLMs, candidates use the same LLMs (there's only like 3 of them) to tweak their resume for better match. I don't know what research you need to see why that makes sense.
Human when preparing a CV: "Make my CV more professional"
LLM many days later presenting a report to HR: "This CV is really professional"
There's probably more to it than that of course.
But it justifies my personal policy of using a different LLM family for code review tasks than for code generation tasks. To avoid the "marking your own homework" problem.
Even taking the tiny bits of the resume that are "hard signal", like GPA, certifications, prior roles, etc, it doesn't translate into their performance in the initial screening interview.
This is why what I think the industry sorely needs is examination consortia.
Rather than trying to guess capability from the name of the university they went to, leading tech companies creating standardized tests in various fields, and your test scores form your "resume", so that developers can just focus on improving their scores rather than wasting time on resume/application/repetitive-screening toil.
Is hits the same spot as that I would take other notes than anyone else and no one could follow them as easily than I do. Everyone leaves the "of course" parts out of the notes if it's for the own use.
Each model likely has its own biases in terms of what constitutes correct corporate speak, and it chooses the resumes that best fit this. Ultimately, I suspect it's more a function of model saying "this grammer, syntax structure, and formatting is most aligned with what is correct corporate language, so flag as high quality".
we are exactly the same
This is a very good reason to avoid using model-generated data to train future models. We'd be deepening this bias by continuing to do that, essentially forcing society to reshape their output using LLMs to increase engagement. This feels like a form of enshittification that doesn't just touch one product but all of society.
You'd make no friends doing it, but as I understand it, for those that have GDPR as a statutory right then under "[Article 22 - Automated individual decision-making, including profiling][0]" you can request to know if your CV was screened by AI and what (and this is key) "meaningful human interaction" led to that decision. Technically this falls under a data subject access request and so a response is mandatory (but who really is going to enforce that - ICO / <insert your data protection agency here> probably isn't). Companies can't just smash a button and claim meaningful interaction, it has to be, well, meaningful and smashing a "nope" button obviously isn't meaninful.
If it turns out that it was only AI that screened it you can request a human review. Do not hold your breath.
Again, you'd make no friends doing it, but sooner or later a test case will emerge to generate some case law around "AI said no" because employment, or lack of because AI says no, does have significant impact on a human.
[0]: https://gdpr.algolia.com/gdpr-article-22
That's the problem right there.
All this shows is that LLMs generate resumes that fit the heuristics LLMs use to judge resumes. And that makes sense, but isn't necessarily a given.
If you are a candidate who wants to be hired, and your target employers use LLMs to filter resumes, then an LLM-generated resume that the employer LLM-powered resume filters favor is "better" — as in "more likely to get you the job".
The AI lacks the ability to extract nuance and implicit information, which means entires end up being long winded and repeatitive. For each requirement its looking for, it must be explicity expressed-- it's quite unnatural, and almost feels like solving a puzzle, to which the obvious solution is to write a comment, then give it and the AI feedback to a failing comment to AI, so it can generate the proper structure the rubric-AI is looking for.
LLMs are statistically driven, and I can only imagine having the AI rewrite the comment produces a result that's more statistically fitting to the model than if any given human were to write it. So, it might mean, yeah, LLMs are better at writing resumes that the LLM can successfully classify-- are they better for a human to consume? Who knows.
No human is going to notice anyway. Or add a N+1 resume written by yourself in which you describe your strategy, just in case.
Further de-duplication is rather easy, and will likely see you black-listed by competant organisations.
Even here on HN many people don’t recognize AI tells that are obvious. Pretty much 100% of all articles posted on HN have been AI generated for months and months already and people don’t seem to care.
I have very little faith in humanity being able to deal with the chaos that LLMs are going to unleash on society.
Heck, most resumes are probably skimmed at best already.
For us, there is some sorting by basic keyword analysis and we start near the top, but there is no proverbial black box that rejects candidates outright.
If candidates are ignored by humans, it’s not because AI rejected them, it’s because we are starting with candidates earlier in the list and might not make it to applicant 537.
Companies are using AI / LLMs to pre-filter resumes. These AIs prefer their own slop resumes. Not just human vs LLMs, but Claude prefers Claude resumes over ChatGPT. Nothing good can come out of that, when resumes are pre-filtered like that.
Unless, of course, you’re not being serious and just trying to be edgy on HN.