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. 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 tools, tasks and reasoning format, and propose an iterative \emph{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 6.8\% relative improvement over the state-of-the-art baseline. Our code and data are available at: \url{https://github.com/RUCAIBOX/ChatCoT}.

翻译：尽管大语言模型（LLMs）在各种评估基准中取得了优异性能，但在需要特定知识和多跳推理的复杂推理任务中仍存在困难。为提升推理能力，我们提出**ChatCoT**——一个面向聊天型大语言模型的工具增强链式推理框架。在ChatCoT中，我们将链式推理建模为多轮对话，通过聊天以更自然的方式使用工具。每一轮对话中，LLMs可以交互工具或执行推理。我们的方法能有效利用聊天型LLMs的多轮对话能力，并以统一方式整合思维链追踪和工具操作。具体而言，我们在对话的早期轮次中通过工具、任务和推理格式进行初始化，并提出迭代的*工具增强推理*步骤以执行逐步的工具增强推理。在两个复杂推理数据集（MATH和HotpotQA）上的实验结果表明，ChatCoT在复杂推理任务上具有有效性，相比当前最优基线实现了6.8%的相对提升。我们的代码和数据已开源：\url{https://github.com/RUCAIBOX/ChatCoT}。