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Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers

Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers

3 October 2024
Shijie Chen
Bernal Jiménez Gutiérrez
Yu Su
ArXivPDFHTML

Papers citing "Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers"

4 / 4 papers shown
Title
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation
Jiashuo Sun
Xianrui Zhong
Sizhe Zhou
Jiawei Han
RALM
33
0
0
12 May 2025
How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective
Qi Liu
Jiaxin Mao
Ji-Rong Wen
LRM
36
1
0
10 Apr 2025
LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking
LLM4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking
Qi Liu
Haozhe Duan
Yiqun Chen
Quanfeng Lu
Weiwei Sun
Jiaxin Mao
37
0
0
10 Apr 2025
Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation
Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation
Abdelrahman Abdallah
Bhawna Piryani
Jamshid Mozafari
Mohammed Ali
Adam Jatowt
96
1
0
21 Feb 2025
1