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

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 Chat-CoT, a tool-augmented chain-of-thought reasoning framework for chat-based LLMs (e.g., ChatGPT).In ChatCoT, we model the chainof-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 chatbased 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 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.Our code and data are available at: https://github.com/RUCAIBOX/ChatCoT.