ConvSDG: Session Data Generation for Conversational Search

arXiv:2403.11335v1 Announce Type: cross
Abstract: 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 …

abstract arxiv conversational conversational search cs.cl cs.ir data fine-tuning however retrieval search search engine session training training data type