---
title: "RetroLLM — paper by Li et al. (2025) — summary and ideas"
description: "To counter this, the paper introduces two mechanisms. 73 references, cited by 11 papers here."
authors: ["Xiaoxi Li", "Jiajie Jin", "Yujia Zhou", "Yongkang Wu", "Zhonghua Li", "Ye Qi", "Zhicheng Dou"]
year: 2025
venue: "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2025.acl-long.819/"
doi: "10.18653/v1/2025.acl-long.819"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2025-retrollm-empowering-large-language-models/"
---
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# RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation

**Authors:** [Xiaoxi Li](https://md.research.elamur.ai/free/en/authors/xiaoxi-li/), [Jiajie Jin](https://md.research.elamur.ai/free/en/authors/jiajie-jin/), [Yujia Zhou](https://md.research.elamur.ai/free/en/authors/yujia-zhou/), [Yongkang Wu](https://md.research.elamur.ai/free/en/authors/yongkang-wu/), [Zhonghua Li](https://md.research.elamur.ai/free/en/authors/zhonghua-li/), [Ye Qi](https://md.research.elamur.ai/free/en/authors/ye-qi/) and [Zhicheng Dou](https://md.research.elamur.ai/free/en/authors/zhicheng-dou/) · **Year:** 2025 · **Venue:** [Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.acl-long.819/) · [PDF](https://aclanthology.org/2025.acl-long.819.pdf) · [DOI 10.18653/v1/2025.acl-long.819](https://doi.org/10.18653/v1/2025.acl-long.819) · [OpenAlex](https://openalex.org/W4412945122)

**Citations:** 8 citations (OpenAlex, 2026-10-14) · [11](https://md.research.elamur.ai/free/en/papers/li-2025-retrollm-empowering-large-language-models/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## 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 University](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) — University, Beijing, China · 333 works · 87,019 citations
- [Renmin University of China](https://md.research.elamur.ai/free/en/universities/renmin-university-of-china/) — University, Beijing, China · 114 works · 16,118 citations
- [Huawei Technologies](https://md.research.elamur.ai/free/en/companies/huawei-technologies/) — Company, Shenzhen, China · 75 works · 12,975 citations

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## 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.

```bibtex
@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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*Updated 31 October 2026*