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.

翻译：摘要：信息性对话旨在通过对话帮助用户收集信息，近年来取得了重大进展。然而，这一研究仍受限于训练数据的稀缺性。为解决此问题，我们提出AutoConv用于合成对话生成，该模型利用了大型语言模型（LLM）的少样本学习能力和生成能力。具体而言，我们将对话生成问题建模为语言建模任务，然后使用少量人工对话微调LLM，以捕获信息性对话过程的特点，并利用其生成高质量的合成对话。在两个常用数据集上的实验结果表明，AutoConv相比强基线模型有显著提升，并减轻了对人工标注的依赖。此外，我们还提供了几项分析研究，以促进未来研究。