1 citations · 2 across the 3 of their papers we have counts for
4 papers
DiffuRank: Effective Document Reranking with Diffusion Language Models
Qi Liu, Kun Ai, Jiaxin Mao +6
Recent advances in large language models (LLMs) have inspired new paradigms for document reranking. While this paradigm better exploits the reasoning and contextual understanding c…
How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
Qi Liu, Jiaxin Mao, Ji-Rong Wen
Recent studies have shown that large language models (LLMs) can assess relevance and support information retrieval (IR) tasks such as document ranking and relevance judgment genera…
LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking
Qi Liu, Haozhe Duan, Yiqun Chen +3
Utilizing large language models (LLMs) for document reranking has been a popular and promising research direction in recent years, many studies are dedicated to improving the perfo…
Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval
Hanqi Zhang, Chong Chen, Lang Mei +2
In the information retrieval (IR) area, dense retrieval (DR) models use deep learning techniques to encode queries and passages into embedding space to compute their semantic relat…