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RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation

Paper · 2025 · Xiaoxi Li, Jiajie Jin, Yujia Zhou et al.

Summary

RetroLLM targets three weaknesses of standard retrieval-augmented generation (RAG): the cost of running a separate retriever, long inputs padded with redundant retrieved chunks, and the fact that retrieval and generation cannot be optimized together. The authors fold both steps into one auto-regressive decoding pass. The LLM first emits short "clues" that must exist in the corpus. These clues select a small set of candidate documents. The model then generates evidence that is copied verbatim from those documents, and finally writes the answer. Copying is enforced through FM-Index-constrained decoding.

A preliminary study showed that constraining evidence against the whole corpus often discards correct paths early in decoding, a problem known as false pruning. To counter this, the paper introduces two mechanisms. Hierarchical FM-Indexes apply corpus-level constraints to clues and document-level constraints to evidence. Forward-looking decoding scores upcoming text windows with a relevance model, so generation is steered by where a prefix leads rather than by the prefix alone.

On five open-domain QA datasets, covering both in-domain and out-of-domain settings, RetroLLM outperforms standard and more complex RAG baselines while consuming far fewer tokens. The work points toward single-model RAG systems that need no external retriever.

Key points

  • Generation format: The decoding sequence passes through three stages:
    • clues inside <|clue|>…<|/clue|>, constrained by a global corpus FM-Index;
    • evidence inside <|evidence|>…<|/evidence|>, constrained by per-document FM-Indexes;
    • the final answer, with no constraints.
  • Document selection: Candidate documents are ranked with a TF-IDF-style score built from the clues' corpus frequency, document frequency and term frequency.
  • Empirical motivation: Prefix relevance was measured with bge-reranker-large. Under corpus-wide constraints it falls sharply within roughly the first 13 tokens. Restricting constraints to relevant documents largely prevents this drop and improves accuracy across beam sizes.
  • Efficiency comparison against Naive RAG, SelfRAG, Iter-RetGen and IRCoT:
    • RetroLLM uses about 315 tokens in total (18 input) versus 919–3002 for the baselines.
    • It reaches an F1 of 49.8, compared with 41.1–48.4 for the baselines.
    • Its latency of 786 ms is below every complex method but somewhat above Naive RAG (582 ms).
  • Stated limitations: Clue expansion is still needed, which blocks fully end-to-end optimization. The authors suggest speculative-decoding-style speedups as future work. Code is available at github.com/sunnynexus/RetroLLM.

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

🎓Tsinghua UniversityBeijing · China333 works · 87,019 citations🎓Renmin University of ChinaBeijing · China114 works · 16,118 citations🏢Huawei TechnologiesShenzhen · China75 works · 12,975 citations

 Fields, topics and concepts

📚 Shelves 6The 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 worksComputer Vision and Pattern Recognition · 2,869 worksInformation Systems · 1,437 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Xiaoxi Li, Jiajie Jin, Yujia Zhou, Yongkang Wu, Zhonghua Li, Ye Qi and Zhicheng Dou. 2025. RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within 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.819

@inproceedings{li2025retrollm,
  title = {{RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation}},
  author = {Xiaoxi Li, Jiajie Jin, Yujia Zhou, 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.819},
  url = {https://doi.org/10.18653/v1/2025.acl-long.819}
}

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