Introducing the Open Chain of Thought Leaderboard
Introducing the Open Chain of Thought Leaderboard
The Open Chain of Thought (CoT) Leaderboard by Hugging Face tracks the effectiveness of large language models (LLMs) in generating reasoning traces for challenging tasks. This leaderboard focuses on the accuracy improvement gained from CoT prompting, contrasting it with traditional methods. By comparing performance with and without CoT, it highlights the impact of structured reasoning in LLMs.
Evaluations include tasks like LogiQA and LSAT, chosen for their relevance and difficulty. Models are assessed using various CoT generation strategies, such as step-by-step reasoning and reflective prompts, with multiple decoding parameters to gauge performance.
Initial findings indicate that smaller, finetuned models can outperform larger ones in specific scenarios, showcasing the nuanced effectiveness of CoT strategies. Future plans include expanding the leaderboard's task range, developing a comprehensive dashboard, and inviting community contributions to enhance this open benchmarking tool.
Contributors can submit models for evaluation, analyze evaluation results, or help develop new CoT strategies and tasks. The Open CoT Leaderboard aims to refine and democratize the assessment of reasoning capabilities in AI.
