ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting

• URL: http://arxiv.org/abs/2403.14312v1
• Date: Thu, 21 Mar 2024 11:34:26 GMT
• Title: ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting
• Authors: Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao, Ji-Rong Wen,
• Abstract summary: Chain-of-Thought (CoT) prompting can enhance the reasoning capabilities of large language models (LLMs) Existing CoT approaches usually focus on simpler reasoning tasks and thus result in low-quality and inconsistent CoT prompts. We introduce CoTGenius, a novel framework designed for the automatic generation of superior CoT prompts.
• Score: 124.69672273754144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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