chain-of-thought-hub
llm-leaderboard
chain-of-thought-hub | llm-leaderboard | |
---|---|---|
10 | 6 | |
2,371 | 266 | |
- | - | |
6.9 | 7.8 | |
10 days ago | 8 months ago | |
Jupyter Notebook | Python | |
MIT License | Apache License 2.0 |
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chain-of-thought-hub
- Chain-Of-Thought Hub: Measuring LLMs' Reasoning Performance
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All Model Leaderboards (that I know)
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
- It looks likely that the MMLU score on Hugginface's LLM leaderboard is wrong after all.
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(2/2) May 2023
Chain-of-Thought Hub: Measuring LLMs' Reasoning Performance (https://github.com/FranxYao/chain-of-thought-hub)
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Ask HN: Is it just me or GPT-4's quality has significantly deteriorated lately?
https://github.com/FranxYao/chain-of-thought-hub
- [N] Chain-of-Thought Hub: Measuring LLMs' Reasoning Performance
- Chain-of-Thought Hub: Measuring LLMs' Reasoning Performance
llm-leaderboard
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Email Obfuscation Rendered Almost Ineffective Against ChatGPT
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/
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Is the ChatGPT and Bing AI boom already over?
palm-2-l-instruct scores 0.909 on Winogrande few-shot.
https://github.com/LudwigStumpp/llm-leaderboard/blob/main/RE...
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Meta is preparing to launch a new open source coding model, dubbed Code Llama, that may release as soon as next week
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
- All Model Leaderboards (that I know)
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GPT-3.5 and GPT-4 performance in Open LLM Leaderboard tests?
Yes, see this leaderboard for a comparison: https://llm-leaderboard.streamlit.app/
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Sharing my comparison methodology for LLM models
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.
What are some alternatives?
DB-GPT - AI Native Data App Development framework with AWEL(Agentic Workflow Expression Language) and Agents
llm-humaneval-benchmarks
tree-of-thoughts - Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%
EvalAI - :cloud: :rocket: :bar_chart: :chart_with_upwards_trend: Evaluating state of the art in AI
airoboros - Customizable implementation of the self-instruct paper.
searchGPT - Grounded search engine (i.e. with source reference) based on LLM / ChatGPT / OpenAI API. It supports web search, file content search etc.
Chinese-LLaMA-Alpaca - 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)
GirlfriendGPT - Girlfriend GPT is a Python project to build your own AI girlfriend using ChatGPT4.0
unilm - Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
gptqlora - GPTQLoRA: Efficient Finetuning of Quantized LLMs with GPTQ
alpa - Training and serving large-scale neural networks with auto parallelization.