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Hierarchical Document Refinement for Long-context Retrieval-augmented Generation

Paper · 2025 · Jiajie Jin, Xiaoxi Li, Guanting Dong et al.

Summary

Retrieval-augmented generation (RAG) often feeds an LLM very long retrieved documents. These texts carry much irrelevant material, which raises inference cost and lowers answer quality. Existing compressors either target short chunks or rank tokens with crude signals such as perplexity, so they ignore how a document is organised. Jin et al. propose LongRefiner, a plug-and-play module that sits between the retriever and the generator. It works in three steps:

  1. It estimates whether a query needs narrow (local) or broad (global) knowledge.
  2. It rebuilds each retrieved text as a tree of sections, subsections and paragraphs, written in compact XML-like markup.
  3. It scores tree nodes in both directions and keeps the best ones within a token budget.

All three tasks are learned as LoRA adapters on a single base model, and part of the work runs offline. On seven QA benchmarks, LongRefiner is competitive with or better than strong baselines. It uses roughly a tenth of the tokens and has about 10x lower cost and latency than the best competitor. This makes long-document RAG cheaper and faster to deploy.

Key points

  • Query analysis: A teacher LLM labels training queries as Local or Global. The refiner learns to emit these labels as special tokens, and a softmax over their probabilities gives a continuous information-scope score.
  • Document structure tags: Four tags represent structure: <section: title>, <subsection: title>, <skip> (marks omitted middle text) and <br> (paragraph break). A parser then maps the output back onto the original document to recover the full tree.
  • Node selection: Local relevance is computed leaf-to-root, and global relevance is computed root-to-leaf over the document outline. The two scores are merged to rank nodes.
  • Results: Evaluation covers seven QA datasets. LongRefiner consistently beats LongLLMLingua across document lengths. Full-content input is better only on short documents, where there is little noise.
  • Scoring models and limits: For local scoring, a reranker works best, embeddings are a faster near-equal option, and term-based scoring falls below the best baseline. Training relies on Wikipedia only, and tables and images are not yet handled. Code is available at github.com/ignorejjj/LongRefiner.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Renmin University of ChinaBeijing · China114 works · 16,118 citations🏢Huawei TechnologiesShenzhen · China75 works · 12,975 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Information retrieval & search · 473 works

Cite

Cite as Jiajie Jin, Xiaoxi Li, Guanting Dong, Yuyao Zhang, Yutao Zhu, Yongkang Wu, Zhonghua Li, Ye Qi and Zhicheng Dou. 2025. Hierarchical Document Refinement for Long-context Retrieval-augmented Generation. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.acl-long.176

@inproceedings{jin2025hierarchical,
  title = {{Hierarchical Document Refinement for Long-context Retrieval-augmented Generation}},
  author = {Jiajie Jin, Xiaoxi Li, Guanting Dong, Yuyao Zhang, Yutao Zhu, Yongkang Wu, Zhonghua Li, Ye Qi and Zhicheng Dou},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.176},
  url = {https://doi.org/10.18653/v1/2025.acl-long.176}
}

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