1 citations · 4 across the 4 of their papers we have counts for
4 papers
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval
Kelong Mao, Chenlong Deng, Haonan Chen +4
Conversational search requires accurate interpretation of user intent from complex multi-turn contexts. This paper presents ChatRetriever, which inherits the strong generalization…
ConvSDG: Session Data Generation for Conversational Search
Fengran Mo, Bole Yi, Kelong Mao +3
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 conversa…
MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model
Le Zhang, Yihong Wu, Fengran Mo +2
Multi-modal open-domain question answering typically requires evidence retrieval from databases across diverse modalities, such as images, tables, passages, etc. Even Large Languag…
Learning to Relate to Previous Turns in Conversational Search
Fengran Mo, Jian-Yun Nie, Kaiyu Huang +4
Conversational search allows a user to interact with a search system in multiple turns. A query is strongly dependent on the conversation context. An effective way to improve retri…