ChainLM: Empowering Large Language Models with Improved Chain-of-Thought
  Prompting
Xiaoxue Cheng Junyi Li Wayne Xin Zhao Ji-Rong Wen
Chain-of-Thought (CoT) prompting can enhance the reasoning capabilities of
large language models (LLMs), establishing itself as a primary approach to
solving complex reasoning tasks. Existing CoT synthesis approaches usually
focus on simpler reasoning tasks and thus result in low-quality and
inconsistent CoT prompts. In response to this challenge, we present an
empirical investigation of CoT prompting and introduce CoTGenius, a novel
framework designed for the automatic generation of superior CoT prompts.
CoTGenius is developed based on three major evolution strategies, i.e.,
complicate, diversify, and specify-alongside two filtering mechanisms:
evolutionary success judgement and correctness verification. We further employ
CoTGenius to create an extensive CoT dataset, and subsequently fine-tune the
Llama 2-Chat 7B and 13B models on this dataset. We call the resulting model
ChainLM. To deal with the cumulative error issue in reasoning steps, we propose
a step-level debating method, wherein multiple debaters discuss each reasoning
step to arrive at the correct answer. Extensive experiments demonstrate that
our ChainLM models exhibit enhanced proficiency in addressing a spectrum of
complex reasoning problems compared to existing models. In addition, we conduct
an in-depth analysis of the impact of data categories within CoTGenius on the
model performance. We release our dataset and code at
https://github.com/RUCAIBox/ChainLM.