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As large language models (LLMs) are continuously being developed, theirevaluation becomes increasingly important yet challenging. This work proposesChain-of-Thought Hub, an open-source evaluation suite on the multi-stepreasoning capabilities of large language models. We are interested in thissetting for two reasons: (1) from the behavior of GPT and PaLM model family, weobserve that complex reasoning is likely to be a key differentiator betweenweaker and stronger LLMs; (2) we envisage large language models to become thenext-generation computational platform and foster an ecosystem of LLM-based newapplications, this naturally requires the foundation models to perform complextasks that often involve the composition of linguistic and logical operations.Our approach is to compile a suite of challenging reasoning benchmarks to trackthe progress of LLMs. Our current results show that: (1) model scale clearlycorrelates with reasoning capabilities; (2) As of May 2023, Claude-v1.3 andPaLM-2 are the only two models that are comparable with GPT-4, whileopen-sourced models still lag behind; (3) LLaMA-65B performs closely tocode-davinci-002, indicating that with successful further development such asreinforcement learning from human feedback (RLHF), it has great potential to beclose to GPT-3.5-Turbo. Our results also suggest that for the open-sourceefforts to catch up, the community may focus more on building better basemodels and exploring RLHF.

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