chain-of-thought-hub VS llm-humaneval-benchmarks

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

You will want to look at HumanEval (https://github.com/abacaj/code-eval) and Eval+ (https://github.com/my-other-github-account/llm-humaneval-ben...) results for coding.

While Llama2 is an improvement over LLaMA v1, it's still nowhere near even the best open models (currently, sans test contamination, WizardCoder-15B, a StarCoder fine tune is at top). It's really not a competition atm though, ChatGPT-4 wipes the floor for coding atm.

Since I've been on a AI code-helper kick recently. According to the post, Claude 2 now 71.2%, a significant upgrade from 1.3 (56.0%). It isn't specified whether this is pass@1 or pass@10.

For comparison:

* GPT-4 claims 85.4 on HumanEval, in a recent paper https://arxiv.org/pdf/2303.11366.pdf GPT-4 was tested at 80.1 pass@1 and 91 pass@1 using their Reflexion technique. They also include MBPP and Leetcode Hard benchmark comparisons

* WizardCoder, a StarCoder fine-tune is one of the top open models, scoring a 57.3 pass@1, model card here: https://huggingface.co/WizardLM/WizardCoder-15B-V1.0

* The best open model I know of atm is replit-code-instruct-glaive, a replit-code-3b fine tune, which scores a 63.5% pass@1. An independent developer abacaj has reproduced that announcement as part of code-eval, a repo for getting human-eval results: https://github.com/abacaj/code-eval

Those interested in this area may also want to take a look at this repo https://github.com/my-other-github-account/llm-humaneval-ben... that also ranks with Eval+, the CanAiCode Leaderboard https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul... and airate https://github.com/catid/supercharger/tree/main/airate

Also, as with all LLM evals, to be taken with a grain of salt...

Liu, Jiawei, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. “Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation.” arXiv, June 12, 2023. https://doi.org/10.48550/arXiv.2305.01210.

To get an idea of programming performance, my can-ai-code Leaderboard is freshly updated this morning, but also check out the excellent llm-eval and code-eval leaderboards.

❗Note: In this study, we copy the scores for HumanEval and HumanEval+ from the LLM-Humaneval-Benchmarks. Notably, all the mentioned models generate code solutions for each problem utilizing a single attempt, and the resulting pass rate percentage is reported. Our WizardCoder generates answers using greedy decoding and tests with the same code.

I switched to RunPod from SageMaker in the middle of this process and boy am I happy I did. It is way cheaper and easier to scale for a project like this, and I highly recommend it. I have a set of tooling to run tests on it en masse now I am happy with - I will try to get my work up on the Github soon!: https://github.com/my-other-github-account/llm-humaneval-benchmarks

Also, my code I used for this eval is up at https://github.com/my-other-github-account/llm-humaneval-benchmarks/tree/8f3a77eb3508f33a88699aac1c4b10d5e3dc7de1