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



@inproceedings{chen-etal-2023-chatcot,
    title = "{C}hat{C}o{T}: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models",
    author = "Chen, Zhipeng  and
      Zhou, Kun  and
      Zhang, Beichen  and
      Gong, Zheng  and
      Zhao, Xin  and
      Wen, Ji-Rong",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.985",
    doi = "10.18653/v1/2023.findings-emnlp.985",
    pages = "14777--14790",
    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.",
}
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            <namePart type="given">Kalika</namePart>
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    <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 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.</abstract>
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%0 Conference Proceedings
%T ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models
%A Chen, Zhipeng
%A Zhou, Kun
%A Zhang, Beichen
%A Gong, Zheng
%A Zhao, Xin
%A Wen, Ji-Rong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chen-etal-2023-chatcot
%X 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.
%R 10.18653/v1/2023.findings-emnlp.985
%U https://aclanthology.org/2023.findings-emnlp.985
%U https://doi.org/10.18653/v1/2023.findings-emnlp.985
%P 14777-14790

Markdown (Informal)

[ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models](https://aclanthology.org/2023.findings-emnlp.985) (Chen et al., Findings 2023)

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