26 citations · 43 across the 37 of their papers we have counts for
13 papers · 1 filter
Learning to Route Queries to Heads for Attention-based Re-ranking with Large Language Models
Yuxing Tian, Fengran Mo, Zhiqi Huang +2
Large Language Models (LLMs) have recently been explored as fine-grained zero-shot re-rankers by leveraging attention signals to estimate document relevance. However, existing meth…
Parametric Retrieval-Augmented Generation using Latent Routing of LoRA Adapters
Zhan Su, Fengran Mo, Jinghan Zhang +3
Parametric Retrieval-Augmented Generation (PRAG) is a RAG approach that integrates external knowledge directly into model parameters using a LoRA adapter, aiming at reducing the in…
Boosting Data Utilization for Multilingual Dense Retrieval
Chao Huang, Fengran Mo, Yufeng Chen +5
Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of di…
Adaptive Personalized Conversational Information Retrieval
Fengran Mo, Yuchen Hui, Yuxing Tian +5
Personalized conversational information retrieval (CIR) systems aim to satisfy users' complex information needs through multi-turn interactions by considering user profiles. Howeve…
ConvMix: A Mixed-Criteria Data Augmentation Framework for Conversational Dense Retrieval
Fengran Mo, Jinghan Zhang, Yuchen Hui +4
Conversational search aims to satisfy users' complex information needs via multiple-turn interactions. The key challenge lies in revealing real users' search intent from the contex…
Conversational Search: From Fundamentals to Frontiers in the LLM Era
Fengran Mo, Chuan Meng, Mohammad Aliannejadi +1
Conversational search enables multi-turn interactions between users and systems to fulfill users' complex information needs. During this interaction, the system should understand t…