Chain-of-Thought Hub: A Continuous Effort to Measure Large Language
  Models' Reasoning Performance
Authors: name: Yao Fu, name: Litu Ou, name: Mingyu Chen, name: Yuhao Wan, name: Hao Peng, name: Tushar Khot

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.