51–60 of 204 posts
I wish the big providers would offer some sort of trial period where you can evaluate models in a _realistic_ setting yourself (i.e cli tools or IDE integrations). I wouldn't even mind strict limits -- just give me two hours or so of usage and I'd already be happy. Seriously. My use-case is probably pretty far from the usual tasks: I'm currently implementing a full observability platform based on VictoriaMetrics / Vi…
Definitely one of the weaker areas in the current LLM boom. Comparing models, or even different versions of the same model, is a pseudo-scientific mess. I'm still using https://lmarena.ai/leaderboard . Perhaps there is something better and someone will pipe up to tell me about it. But we use LLMs at work and have unexplainable variations between them. And when we get a prompt working reliably on one model, we often h…
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I don’t think the fact that LLMs can handle small numbers more reliably has anything to do with their reasoning ability. To the contrary, reasoning ability should enable them to handle numbers of arbitrary size, just as it enables humans to do so, given some pencil and paper. However: > Testing only on these problems would not predict performance on larger numbers, where LLMs struggle. Since performance on large numb…
> Since performance on large numbers is not what these exams are intended to test for, How so? Isn't the point of these exams to test arithmetic skills? I would hope we'd like arithmetic skills to be at a constant level regardless of the size of the number?
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Any argument to back up your claim?
Not the person you replied to, but I'm a programmer and up until ~3 weeks ago I really only used AI for auto-complete, looking up API information, and constructing arcane CLI commands. I decided to take a leap and use AI as much as possible to complete a ticket at work. Now, 3 weeks later AI is writing 90% of my code. Granted, I'm not sitting back sipping on a latte while AI does my job. It's a very interactive proce…
"For example, if a benchmark reuses questions from a calculator-free exam such as AIME," the study says, "numbers in each problem will have been chosen to facilitate basic arithmetic. Testing only on these problems would not predict performance on larger numbers, where LLMs struggle."
At least some of those who design benchmark tests are aware of these concerns.
If anyone doubts my concerns and thinks this article is in good faith, just check out this site's "AI+ML" section: https://www.theregister.com/software/ai_ml/
Even if it requires human evaluators at first, and even if the models completely suck at this task right now: it seems like the kind of task you'd want them to be good at, if you want these models to eventually carry out these tasks in embodied forms in the real world.
Just having the benchmark in the first place is what gives model makers something to optimize for.
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For what it's worth, I work on platforms infra at a hyperscaler and benchmarks are a complete fucking joke in my field too lol. Ultimately we are measuring extremely measurable things that have an objective ground truth. And yet: - we completely fail at statistics (the MAJORITY of analysis is literally just "here's the delta in the mean of these two samples". If I ever do see people gesturing at actual proper analysi…
Even a p-value is insufficient. Maybe can use some of this stuff https://web.stanford.edu/~swager/causal_inf_book.pdf
It's not like there's a shortage of skills in this area, it seems like our one specific industry just has a weird blindspot.
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I don’t think the fact that LLMs can handle small numbers more reliably has anything to do with their reasoning ability. To the contrary, reasoning ability should enable them to handle numbers of arbitrary size, just as it enables humans to do so, given some pencil and paper. However: > Testing only on these problems would not predict performance on larger numbers, where LLMs struggle. Since performance on large numb…
> reasoning ability should enable them to handle numbers of arbitrary size, just as it enables humans to do so, given some pencil and paper. Or given a calculator. Which it's running on. Which it in some sense is . There's something deeply ironic about the fact that we have an "AI" running on the most technologically advanced calculator in the history of mankind and...it can't do basic math.
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A/B testing is radioactive too. It's indirectly optimizing for user feedback - less stupid than directly optimizing for user feedback, but still quite dangerous. Human raters are exploitable, and you never know whether the B has a genuine performance advantage over A, or just found a meat exploit by an accident. It's what fucked OpenAI over with 4o, and fucked over many other labs in more subtle ways.
Are you talking about just preferences or A/B tests on like retention and engagement? The latter I think is pretty reliable and powerful though I have never personally done them. Preferences are just as big a mess: WHO the annotators are matters, and if you are using preferences as a proxy for like correctness, you’re not really measuring correctness you’re measuring e.g. persuasion. A lot of construct validity chall…
GPT-4o's endless sycophancy was great for retention, GPT-5's style of ending every response in a question is great for engagement.
Are those desirable traits though? Doubt it. They look like simple tricks and reek of reward hacking - and A/B testing rewards them indeed. Direct optimization is even worse. Combining the two is ruinous.
Mind, I'm not saying that those metrics are useless. Radioactive materials aren't useless. You just got to keep their unpleasant properties in mind at all times - or suffer the consequences.
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Pencil and paper is just testing with tools enabled.
I’d say it’s fair for LLMs to be able to use any tool in benchmarks, so long as they are the ones to decide to use them.