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.

利用大型语言模型（LLM）的少样本学习能力和生成能力，我们提出了AutoConv用于合成对话生成，将对话生成问题形式化为语言建模任务，然后使用少量人类对话经过微调训练LLM以捕捉信息获取过程的特征，并使用它生成高质量的合成对话，验证实验表明AutoConv大大改进了目前基线模型的性能，并减少了对人类注释的依赖，此外，我们还提供了多项分析研究以促进未来的研究。

AutoConv: 使用大型语言模型自动生成信息检索对话