Hacker News Reader

Comment by apetresc | Hacker News Reader

I just find it hilarious how approximately 100% of models beat all other models on benchmarks.

Benchmarks published by the company itself should be treated no differently than advertising. For actual signal check out more independent leaderboards and benchmarks (like HuggingFace, Chatbot Arena, MMLU, AlpacaEval). Of course, even then it is impossible to come up with an objective ranking since there is no consensus on what to even measure.

Just because of the pace of innovation and scaling, right now, it seems pretty natural that any new model is going to be better than the previous comparable models.

Benchmarks are often weird because of what a benchmark inherently needs to be.

If you compare LLMs by asking them to tell you how to catch dragonflies - the free text chat answer you get will be impossible to objectively evaluate.

Whereas if you propose four ways to catch dragonflies and ask each model to choose option A, B, C or D (or check the relative probability the model assigns to those four output logits) the result is easy to objectively evaluate - you just check if it chose the one right answer.

Hence a lot of the most famous benchmarks are multiple-choice questions - even though 99.9% of LLM usage doesn't involve answering multiple-choice questions.

gotta cherry pick your benchmarks as much as possible

What do you mean?

Virtually every announcement of a new model release has some sort of table or graph matching it up against a bunch of other models on various benchmarks, and they're always selected in such a way that the newly-released model dominates along several axes.

It turns interpreting the results into an exercise in detecting which models and benchmarks were omitted.

It would make sense, wouldn't it? Just as we've seen rising fuel efficiency, safety, dependability, etc. over the lifecycle of a particular car model.

The different teams are learning from each other and pushing boundaries; there's virtually no reason for any of the teams to release a model or product that is somehow inferior to a prior one (unless it had some secondary attribute such as requiring lower end hardware).

We're simply not seeing the ones that came up short; we don't even see the ones where it fell short of current benchmarks because they're not worth releasing to the public.

That's a valid theory, a priori, but if you actually follow up you'll find that the vast majority of these benchmark results don't end up matching anyone's subjective experience with the models. The churn at the top is not nearly as fast as the press releases make it out to be.

Subjective experience is not a benchmark that you can measure success against. Also, of course new models are better on some set of benchmarks. Why would someone bother releasing a "new" model that is inferior to old ones? (Aside from attributes like more preferable licensing).

This is completely normal, the opposite would be strange.

Sibling comment made a good point about benchmarks not being a great indiactor of real world quality. Every time something scores near GPT-4 on benchmarks, I try it out and it ends up being less reliable than GPT-3 within a few minutes of usage.

That's totally fine, but benchmarks are like standardized tests like the SAT. They measure something and it totally makes sense that each release bests the prior in the context of these benchmarks.

It may even be the case that in measuring against the benchmarks, these product teams sacrifice some real world performance (just as a student that only studies for the SAT might sacrifice some real world skills).

Reply on news.ycombinator.com