ConvSDG: Session Data Generation for Conversational Search

Understand

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.