Conversational search provides a more convenient interface for users to search by allowing multi-turn interaction with the search engine. However, the effectiveness of the conversational dense retrieval methods is limited by the scarcity of training data required for their fine-tuning. Thus, generating more training conversational sessions with relevant labels could potentially improve search performance. Based on the promising capabilities of large language models (LLMs) on text generation, we propose ConvSDG, a simple yet effective framework to explore the feasibility of boosting conversational search by using LLM for session data generation. Within this framework, we design dialogue/session-level and query-level data generation with unsupervised and semi-supervised learning, according to the availability of relevance judgments. The generated data are used to fine-tune the conversational dense retriever. Extensive experiments on four widely used datasets demonstrate the effectiveness and broad applicability of our ConvSDG framework compared with several strong baselines.

翻译：对话式搜索通过允许用户与搜索引擎进行多轮交互，为搜索提供了更便捷的界面。然而，对话式稠密检索方法的有效性受到微调所需训练数据稀缺的限制。因此，生成更多带有相关标签的训练会话数据，有望提升搜索性能。基于大语言模型在文本生成方面的强大能力，我们提出了ConvSDG，一个简单而有效的框架，旨在探索利用大语言模型生成会话数据来增强对话式搜索的可行性。在该框架中，我们根据相关性标注的可用性，设计了基于无监督和半监督学习的对话/会话级别与查询级别的数据生成方法。生成的数据用于微调对话式稠密检索器。在四个广泛使用的数据集上进行的大量实验表明，与若干强基线方法相比，我们的ConvSDG框架具有出色的有效性和广泛的适用性。