Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance

• URL: http://arxiv.org/abs/2305.17306v1
• Date: Fri, 26 May 2023 23:46:42 GMT
• Title: Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance
• Authors: Yao Fu, Litu Ou, Mingyu Chen, Yuhao Wan, Hao Peng and Tushar Khot
• Abstract summary: Chain-of-Thought Hub is an open-source evaluation suite on the multi-step reasoning capabilities of large language models. This work proposes Chain-of-Thought Hub, an open-source evaluation suite on the multi-step reasoning capabilities of large language models.
• Score: 35.38549845444575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models (LLMs) are continuously being developed, their evaluation becomes increasingly important yet challenging. This work proposes Chain-of-Thought Hub, an open-source evaluation suite on the multi-step reasoning capabilities of large language models. We are interested in this setting for two reasons: (1) from the behavior of GPT and PaLM model family, we observe that complex reasoning is likely to be a key differentiator between weaker and stronger LLMs; (2) we envisage large language models to become the next-generation computational platform and foster an ecosystem of LLM-based new applications, this naturally requires the foundation models to perform complex tasks that often involve the composition of linguistic and logical operations. Our approach is to compile a suite of challenging reasoning benchmarks to track the progress of LLMs. Our current results show that: (1) model scale clearly correlates with reasoning capabilities; (2) As of May 2023, Claude-v1.3 and PaLM-2 are the only two models that are comparable with GPT-4, while open-sourced models still lag behind; (3) LLaMA-65B performs closely to code-davinci-002, indicating that with successful further development such as reinforcement learning from human feedback (RLHF), it has great potential to be close to GPT-3.5-Turbo. Our results also suggest that for the open-source efforts to catch up, the community may focus more on building better base models and exploring RLHF.

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