chain-of-thought-hub VS llm-leaderboard

Chain-of-Thought Hub https://github.com/FranxYao/chain-of-thought-hub - these are mostly gathered although Yao Fu, the author is working on specific CoT runs

Chain-of-Thought Hub: Measuring LLMs' Reasoning Performance (https://github.com/FranxYao/chain-of-thought-hub)

https://github.com/FranxYao/chain-of-thought-hub

This is assuming you’re using a really big LLM behind a paid service. There are plenty of smaller open source models. Not sure at what point it’s not “large” but when fine tuned they are capable of matching the largest LLM in performance on narrow tasks.

Some of these open source models can even be run on your local machine. It’d be very inexpensive to run thousands of pages through it.

https://llm-leaderboard.streamlit.app/

palm-2-l-instruct scores 0.909 on Winogrande few-shot.

https://github.com/LudwigStumpp/llm-leaderboard/blob/main/RE...

They said it "rivals OpenAI’s Codex model" which performs worse than starcoder-16b on HumanEval-Python (pass@1) according to https://github.com/LudwigStumpp/llm-leaderboard

Yes, see this leaderboard for a comparison: https://llm-leaderboard.streamlit.app/

So I've tried to use a basic matrix factorization method to estimate unknown benchmark scores for models based on the known benchmark scores. Basically, I assume each model has some intrinsic "quality" score, and the known benchmarks are assumed to be a linear function of the quality score. This is similar to matrix factorization with only 1 latent factor (though the bias values have to handled differently). Then I fit the known benchmark scores from https://github.com/LudwigStumpp/llm-leaderboard to my parameters, and estimate the remaining benchmark scores.