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HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88

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Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88

#121

Post by syockit »

srdjanr wrote: post #100
neya wrote: post #25

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 s…

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.

Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88

#122

Post by vbarrielle »

jstanley wrote: post #114
317070 wrote: post #47

Earlier quoted context omitted.

> 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…

> "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.

Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88

#123

Post by realty_geek »

gebruikersnaam wrote: post #84
realty_geek wrote: post #82

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, b…

The article raises a lot of questions the article already answered.

keine ahnung

Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88

#124

Post by vbarrielle »

DougBTX wrote: post #112
317070 wrote: post #47

Earlier quoted context omitted.

> 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…

> 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.

Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88

#126

Post by tipsytoad »

nok22kon wrote: post #113
dvt wrote: post #6

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…

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

Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88

#127

Post by LinXitoW »

sevenzero wrote: post #27
jerrythegerbil wrote: post #18

Earlier quoted context omitted.

> 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... 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.

Re: HackerRank open sourced its ATS. My resume scored 90/100. Oh wait 74. No – 88

#128

Post by embedding-shape »

tipsytoad wrote: post #126
nok22kon wrote: post #113

Earlier quoted context omitted.

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

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.
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