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home / papers / 2026 / Don't Be Misled by Style: A Style-Adaptive Reranker for Capturing Effective Knowledge in Retrieval-Augmented Generation

Don't Be Misled by Style: A Style-Adaptive Reranker for Capturing Effective Knowledge in Retrieval-Augmented Generation

Paper · 2026 · Ruwen Zhang, Bo Liu, Zhang Sheng Xiang et al.

Summary

RAG systems increasingly draw passages from sources with very different writing styles, such as encyclopedias, news, and social media. Most rerankers, however, are trained on carefully edited text, so they can treat a formal register as a proxy for relevance. Useful evidence written casually then gets pushed down the ranking. The authors confirm this with Natural Questions: they pair each passage with an informal rewrite that keeps the content the same, and every reranker they test loses downstream answer quality on the informal versions.

To address this, they propose SARK (Style-Adaptive Reranker with Knowledge Prioritization). An LLM serves as a verifier and produces two kinds of supervision:

  • Passage-level labels: whether a passage fixes or breaks the model's answer compared with answering without retrieval.
  • List-level preference orders: a ranking over candidate passages, obtained through multi-turn prompting.

The reranker is trained on both objectives jointly, over formal/informal pairs of the same passage, so that its scores track content rather than style. SARK improves generation quality for several LLM backbones in mixed-style settings and runs at roughly encoder-level cost. This makes it a practical plug-in component for real-world RAG pipelines.

Key points

  • Style sensitivity of existing rerankers: BGE-Base, BGE-Large, MiniLM, mGTE, RankGPT and RankLlama were tested on Top-50 NQ passages. Switching to the informal variants lowered LLM answer accuracy by about 4.99% and BERTScore by about 2.84% on average.
  • New dataset: the authors build a multi-style reranking dataset of paired formal/informal passages. It carries passage-level supportive/misleading labels and list-level ranking orders, and its quality was checked by four human annotators.
  • How passages are labeled:
    • Positive: the LLM fails without context but answers correctly with the passage.
    • Hard negative: the LLM is correct on its own but fails once the passage is added.
  • Efficiency:
    • SARK runs at 127.89 ms/query and 7.82 queries/sec, comparable to BGE-Large.
    • This is over 10× faster than RankLLaMA.
    • MiniLM is the fastest model tested at 39.59 queries/sec.
  • Stated limitations:
    • Supervision comes from a single source LLM, which may not transfer to other architectures.
    • Style is reduced to a formal vs. informal split, leaving dimensions such as tone and discourse structure unexplored.

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

🎓Southeast UniversityNanjing · China28 works · 354 citations🔬Purple Mountain LaboratoriesNanjing · China2 works · 1 citation

 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 Ruwen Zhang, Bo Liu, Zhang Sheng Xiang, Yida Chen, Hantao Zhao, Ding Ding, Jiahui Jin and Jiuxin Cao. 2026. Don't Be Misled by Style: A Style-Adaptive Reranker for Capturing Effective Knowledge in Retrieval-Augmented Generation. Proceedings of the 64th 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/v1/2026.acl-long.302

@inproceedings{zhang2026don,
  title = {{Don't Be Misled by Style: A Style-Adaptive Reranker for Capturing Effective Knowledge in Retrieval-Augmented Generation}},
  author = {Ruwen Zhang, Bo Liu, Zhang Sheng Xiang, Yida Chen, Hantao Zhao, Ding Ding, Jiahui Jin and Jiuxin Cao},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.302},
  url = {https://doi.org/10.18653/v1/2026.acl-long.302}
}

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