  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.
