Feb. 13, 2024, 5:48 a.m. | Haonan Chen Zhicheng Dou Kelong Mao Jiongnan Liu Ziliang Zhao

cs.CL updates on arXiv.org arxiv.org

Conversational search utilizes muli-turn natural language contexts to retrieve relevant passages. Existing conversational dense retrieval models mostly view a conversation as a fixed sequence of questions and responses, overlooking the severe data sparsity problem -- that is, users can perform a conversation in various ways, and these alternate conversations are unrecorded. Consequently, they often struggle to generalize to diverse conversations in real-world scenarios. In this work, we propose a framework for generalizing Conversational dense retrieval via LLM-cognition data Augmentation (ConvAug). …

augmentation cognition conversation conversational conversational search conversations cs.cl cs.ir data language llm natural natural language questions responses retrieval search sparsity via view

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