  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 ChatCoT, a tool-augmented chain-of-thought
reasoning framework for chat-based LLMs (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 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: \url{https://github.com/RUCAIBOX/ChatCoT}.
