AI leaderboards are no longer useful. It's time to switch to Pareto curves
1–10 of 17 posts
Re: AI leaderboards are no longer useful. It's time to switch to Pareto curves
#2This is the most applicable part of the article:
Strategies to improve LLM accuracy:
Retry: We repeatedly invoke a model with the temperature set to zero, up to five times, if it fails the test cases provided with the problem description. Retrying makes sense because LLMs aren’t deterministic even at temperature zero.
Warming: This is the same as the retry strategy, but we gradually increase the temperature of the underlying model with each run, from 0 to 0.5. This increases the stochasticity of the model and, we hope, increases the likelihood that at least one of the retries will succeed.
Escalation: We start with a cheap model (Llama-3 8B) and escalate to more expensive models (GPT-3.5, Llama-3 70B, GPT-4) if we encounter a test case failure.
Re: AI leaderboards are no longer useful. It's time to switch to Pareto curves
#3Alternative title: AI leaderboards would be useful if they didn't blindly believe the author's benchmarks, included good baselines, and factored in real cost to run the model (parameter count can be misleading). Pareto curves are a good tool to decide which model is the best for a given price/performance tradeoff and should be used more
But that's not quite as catchy. Great article
Re: AI leaderboards are no longer useful. It's time to switch to Pareto curves
#4[deleted]
Re: AI leaderboards are no longer useful. It's time to switch to Pareto curves
#5This is the most applicable part of the article: Strategies to improve LLM accuracy: Retry: We repeatedly invoke a model with the temperature set to zero, up to five times, if it fails the test cases provided with the problem description. Retrying makes sense because LLMs aren’t deterministic even at temperature zero. Warming: This is the same as the retry strategy, but we gradually increase the temperature of the un…
> Retrying makes sense because LLMs aren’t deterministic even at temperature zero.
This is news to me. I'm trying to think where non-determinism would come in at temperature zero, but coming up with nothing. What am I missing?
Re: AI leaderboards are no longer useful. It's time to switch to Pareto curves
#6I have advocated and used pareto fronts as a model selection method for ML for a long while. It's really useful to construct two tests - hard but important, and run of the mill - and plot model performance against each one and draw a pareto front so you can see which of your models are off the edge. In fact if you were to look at Figure 8.3 in "Managing Machine Learning Projects" then you would see this kind of thing!
But, I'm just an old bot shilling for my product.
Re: AI leaderboards are no longer useful. It's time to switch to Pareto curves
#7This is the most applicable part of the article: Strategies to improve LLM accuracy: Retry: We repeatedly invoke a model with the temperature set to zero, up to five times, if it fails the test cases provided with the problem description. Retrying makes sense because LLMs aren’t deterministic even at temperature zero. Warming: This is the same as the retry strategy, but we gradually increase the temperature of the un…
These strategies seem immediately practical. If you want to go beyond zero-shot for LLM coding, you may not need a complicated agent architecture - just start with escalation, retry, and warming.
Re: AI leaderboards are no longer useful. It's time to switch to Pareto curves
#8This is the most applicable part of the article: Strategies to improve LLM accuracy: Retry: We repeatedly invoke a model with the temperature set to zero, up to five times, if it fails the test cases provided with the problem description. Retrying makes sense because LLMs aren’t deterministic even at temperature zero. Warming: This is the same as the retry strategy, but we gradually increase the temperature of the un…
> Retrying makes sense because LLMs aren’t deterministic even at temperature zero. This is news to me. I'm trying to think where non-determinism would come in at temperature zero, but coming up with nothing. What am I missing?
It can happen due to a number of reasons, but in the case of GPT-4 it's probably because of their MoE implementation
Re: AI leaderboards are no longer useful. It's time to switch to Pareto curves
#9This is the most applicable part of the article: Strategies to improve LLM accuracy: Retry: We repeatedly invoke a model with the temperature set to zero, up to five times, if it fails the test cases provided with the problem description. Retrying makes sense because LLMs aren’t deterministic even at temperature zero. Warming: This is the same as the retry strategy, but we gradually increase the temperature of the un…
> Retrying makes sense because LLMs aren’t deterministic even at temperature zero. This is news to me. I'm trying to think where non-determinism would come in at temperature zero, but coming up with nothing. What am I missing?
It's because floating-point arithmetic isn't deterministic, which becomes salient when (speaking loosely) the difference between likelihood of two different tokens is less than the precision of the FPU.
I am not sure to what extent this effect has been quantified.
Re: AI leaderboards are no longer useful. It's time to switch to Pareto curves
#10I had a very similar comment last month - albeit more ignorant and less helpful: https://news.ycombinator.com/item?id=39957153
Basically, none of these agentic / MoE / etc papers have actually compared their results to the naive baseline: since these are nondeterministic programs, Randomized Algortihms 101 tells you that if the probability of success is sufficiently high, you can improve performance simply by running the algorithm multiple times and taking the majority/plurality result.
So is MoE or agents actually more effective than doing it the dumb way? AI Snake Oil says "no." Truly bizarre that dozens of researchers didn't even ask! It made me feel like I was missing something.