18 citations · 73 across the 17 of their papers we have counts for
17 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…
An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models
Qi Liu, Gang Guo, Jiaxin Mao +5
With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and…
UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models
Zhaoheng Huang, Zhicheng Dou, Yutao Zhu +1
Large language models (LLMs) may generate text that lacks consistency with human knowledge, leading to factual inaccuracies or \textit{hallucination}. Existing research for evaluat…
Metacognitive Retrieval-Augmented Large Language Models
Yujia Zhou, Zheng Liu, Jiajie Jin +2
Retrieval-augmented generation have become central in natural language processing due to their efficacy in generating factual content. While traditional methods employ single-time…
Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism
Yujia Zhou, Qiannan Zhu, Jiajie Jin +1
Traditional search engines usually provide identical search results for all users, overlooking individual preferences. To counter this limitation, personalized search has been deve…
Grounding Language Model with Chunking-Free In-Context Retrieval
Hongjin Qian, Zheng Liu, Kelong Mao +2
This paper presents a novel Chunking-Free In-Context (CFIC) retrieval approach, specifically tailored for Retrieval-Augmented Generation (RAG) systems. Traditional RAG systems ofte…