"[...] we talk about LMSys's because they agree with our feelings, and we don't talk about other results because they do not."
What other benchmarks do you think are being overlooked?
What other benchmarks do you think are being overlooked?
I recently saw a paper getting LLM evaluation completely right in many respects: https://arxiv.org/abs//2402.00861
"Evaluating Large Language Models for Generalization and Robustness via Data Compression"
They evaluate LLMs by seeing how well they compress text/news/science from the future, over time. In a sense, this measures how "surprised" the LLM is by future developments.
This is, I believe, an important and strong task. A top researcher or economist will have a preternatural ability to "see where the puck is going" in a way that would be more surprising (perplexing) to a junior person or a layperson. Thus, things after the knowledge cutoff being more "expected" is a great sign of predictive generality.
LMSys has the same energy as Amazon reviews of brand name products. It's measuring something, and that something might be valuable. I don't know if it is valuable for answering subjective, spicy questions like, "What LLM is likely to replace most or all of your job right now, giving you some time to arbitrage 1h of chatbot with a full time employment?" which is what everyone actually cares about. That is what this is all about. I can't tell you what benchmark is answering that question or others. I don't know!
The other one I consult occasionally is https://gorilla.cs.berkeley.edu/leaderboard.html - the Berkeley Function-Calling Leaderboard - because function calling is a really useful characteristic of smaller models.
I believe the best ways to evaluate these models is with your own set of task-specific evals for your own project, but these are REALLY difficult to construct. I still don't have a good solution there for my own work.
you might find it useful
https://github.com/carlini/yet-another-applied-llm-benchmark