6 citations · 9 across the 11 of their papers we have counts for
4 papers · 1 filter
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…
Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models
Qi Liu, Bo Wang, Nan Wang +1
Recent studies have demonstrated the effectiveness of using large language language models (LLMs) in passage ranking. The listwise approaches, such as RankGPT, have become new stat…
TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy
Yiqun Chen, Qi Liu, Yi Zhang +6
Large Language Models (LLMs) are increasingly employed in zero-shot documents ranking, yielding commendable results. However, several significant challenges still persist in LLMs f…
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…