ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models

Zhipeng Chen, Kun Zhou, Beichen Zhang, Zheng Gong, Xin Zhao, Ji-Rong Wen

Submission Type: Regular Long Paper

Submission Track: NLP Applications

Keywords: large language models, complex reasoning, chain-of-thought

Abstract:

Although large language models (LLMs) have achieved excellent performance in a variety of evaluation benchmarks, they still struggle in complex reasoning tasks which require specific knowledge and multi-hop reasoning.
To improve the reasoning abilities, we propose $\textbf{ChatCoT}$, a tool-augmented chain-of-thought reasoning framework for chat-based LLMs ($\textit{e.g.,}$ ChatGPT).
In ChatCoT, we model the chain-of-thought~(CoT) reasoning as multi-turn conversations, to utilize tools in a more natural way through chatting.
At each turn, LLMs can either interact with tools or perform the reasoning.
Our approach can effectively leverage the multi-turn conversation ability of chat-based LLMs, and integrate the thought chain following and tools manipulation in a unified way.
Specially, we initialize the early turns of the conversation by the knowledge about tools, tasks, and reasoning format, and propose an iterative $\textit{tool-augmented reasoning}$ step to perform step-by-step tool-augmented reasoning.
The experiment results on two complex reasoning datasets (MATH and HotpotQA) have shown the effectiveness of ChatCoT on complex reasoning tasks, achieving a 7.9\% relative improvement over the state-of-the-art baseline.

Submission Number: 722