Hierarchical Document Refinement for Long-context Retrieval-augmented Generation
Jiajie Jin, Xiaoxi Li, Guanting Dong, Yuyao Zhang, Yutao Zhu, Yongkang Wu, Zhonghua Li, Ye Qi
Abstract
Real-world RAG applications often encounter long-context input scenarios, where redundant information and noise results in higher inference costs and reduced performance. To address these challenges, we propose LongRefiner, an efficient plug-and-play refiner that leverages the inherent structural characteristics of long documents. LongRefiner employs dual-level query analysis, hierarchical document structuring, and adaptive refinement through multi-task learning on a single foundation model. Experiments on seven QA datasets demonstrate that LongRefiner achieves competitive performance in various scenarios while using 10x fewer computational costs and latency compared to the best baseline. Further analysis validates that LongRefiner is scalable, efficient, and effective, providing practical insights for real-world long-text RAG applications. Our code is available at https://github.com/ignorejjj/LongRefiner.
BibTeX
@inproceedings{jin-etal-2025-hierarchical,
title = "Hierarchical Document Refinement for Long-context Retrieval-augmented Generation",
author = "Jin, Jiajie and
Li, Xiaoxi and
Dong, Guanting and
Zhang, Yuyao and
Zhu, Yutao and
Wu, Yongkang and
Li, Zhonghua and
Qi, Ye and
Dou, Zhicheng",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.176/",
doi = "10.18653/v1/2025.acl-long.176",
pages = "3502--3520",
ISBN = "979-8-89176-251-0"
}