@inproceedings{li-etal-2023-autoconv,
    title = "{A}uto{C}onv: Automatically Generating Information-seeking Conversations with Large Language Models",
    author = "Li, Siheng  and
      Yang, Cheng  and
      Yin, Yichun  and
      Zhu, Xinyu  and
      Cheng, Zesen  and
      Shang, Lifeng  and
      Jiang, Xin  and
      Liu, Qun  and
      Yang, Yujiu",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-short.149",
    doi = "10.18653/v1/2023.acl-short.149",
    pages = "1751--1762",
    abstract = "Information-seeking conversation, which aims to help users gather information through conversation, has achieved great progress in recent years. However, the research is still stymied by the scarcity of training data. To alleviate this problem, we propose AutoConv for synthetic conversation generation, which takes advantage of the few-shot learning ability and generation capacity of large language models (LLM). Specifically, we formulate the conversation generation problem as a language modeling task, then finetune an LLM with a few human conversations to capture the characteristics of the information-seeking process and use it for generating synthetic conversations with high quality. Experimental results on two frequently-used datasets verify that AutoConv has substantial improvements over strong baselines and alleviates the dependence on human annotation. In addition, we also provide several analysis studies to promote future research.",
}

Information-seeking conversation, which aims to help users gather information through conversation, has achieved great progress in recent years. However, the research is still stymied by the scarcity of training data. To alleviate this problem, we propose AutoConv for synthetic conversation generation, which takes advantage of the few-shot learning ability and generation capacity of large language models (LLM). Specifically, we formulate the conversation generation problem as a language modeling task, then finetune an LLM with a few human conversations to capture the characteristics of the information-seeking process and use it for generating synthetic conversations with high quality. Experimental results on two frequently-used datasets verify that AutoConv has substantial improvements over strong baselines and alleviates the dependence on human annotation. In addition, we also provide several analysis studies to promote future research.
Li, Siheng Yang, Cheng Yin, Yichun Zhu, Xinyu Cheng, Zesen Shang, Lifeng Jiang, Xin Liu, Qun Yang, Yujiu
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models