---
title: "GenProve — paper by Wei et al. (2026) — summary and ideas"
description: "The authors define a task called Generation-time Fine-grained Provenance. 36 references."
authors: ["Jingxuan Wei", "Xingyue Wang", "Yanghaoyu Liao", "Jie Dong", "Yuchen Liu", "Caijun Jia", "Bihui Yu", "Junnan Zhu"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.228/"
doi: "10.18653/v1/2026.acl-long.228"
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/wei-2026-genprove-learning-generate-text-fine/"
---
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# GenProve: Learning to Generate Text with Fine-Grained Provenance

**Authors:** [Jingxuan Wei](https://md.research.elamur.ai/free/en/authors/jingxuan-wei/), Xingyue Wang, Yanghaoyu Liao, Jie Dong, [Yuchen Liu](https://md.research.elamur.ai/free/en/authors/yuchen-liu/), Caijun Jia, [Bihui Yu](https://md.research.elamur.ai/free/en/authors/bihui-yu/) and [Junnan Zhu](https://md.research.elamur.ai/free/en/authors/junnan-zhu/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **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/2026.acl-long.228/) · [PDF](https://aclanthology.org/2026.acl-long.228.pdf) · [DOI 10.18653/v1/2026.acl-long.228](https://doi.org/10.18653/v1/2026.acl-long.228) · [OpenAlex](https://openalex.org/W7166857498)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary

Citations in LLM answers often point only to whole documents. They do not show which sentence backs a claim or how that sentence is used, so checking the answer stays hard. The authors define a task called Generation-time Fine-grained Provenance. The model writes an answer and, for each sentence, emits structured triples: document ID, sentence ID, and a relation label (Quotation, Compression, or Inference).

To support training, they build ReFInE on an existing long-form, multi-document QA corpus (Yehudai et al., 2024). Sentences are split and indexed, GPT-4o proposes provenance labels, and experts verify the results over several stages. The GenProve framework first applies SFT and then GRPO. GRPO uses a combined reward that scores answer similarity and provenance correctness (PROVE-F1).

GenProve outperforms 14 strong LLMs in joint evaluation of answer quality and provenance accuracy. Analysis reveals a "reasoning gap": models handle quotation well but have much more trouble with compression and inference. This points to verifiable reasoning as an open problem, separate from surface-level citation.

## Key points

- **Relation labels:** ReFInE adopts the TROVE relation taxonomy (Quotation, Compression, Inference) but drops TROVE's "Other" label. The authors consider it rare and too vague to optimize toward.
- **Data splits:** ReFInE is divided roughly into SFT (55.4%), GRPO (23.22%), and evaluation (21.38%) portions.
- **Difficulty by relation:** Per-relation F1 shows Quotation is the easiest. GenProve's largest improvements come on the harder Compression and Inference cases.
- **Typical error:** The model often finds the right evidence but assigns it the wrong relation type.
- **Evaluation validity:** LLM-as-a-Judge scores correlate strongly with human ratings at the model level (Pearson r = 0.9395).
- **Limitations and code:** The dataset is English-only, the triples add output length and latency, and results depend on retriever quality. Code is published at github.com/yanghaoyuliao/GenProve.

*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

- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 194 works · 23,906 citations
- [Institute of Automation](https://md.research.elamur.ai/free/en/labs/institute-of-automation/) — Research institute, Beijing, China · 70 works · 14,251 citations
- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 147 works · 8,480 citations
- [Shenyang Institute of Computing Technology](https://md.research.elamur.ai/free/en/companies/shenyang-institute-of-computing-technology/) — Company, Shenyang, China · 2 works · 19 citations

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## Cite

Cite as: Jingxuan Wei, Xingyue Wang, Yanghaoyu Liao, Jie Dong, Yuchen Liu, Caijun Jia, Bihui Yu and Junnan Zhu. 2026. GenProve: Learning to Generate Text with Fine-Grained Provenance. 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.

```bibtex
@inproceedings{wei2026genprove,
  title = {{GenProve: Learning to Generate Text with Fine-Grained Provenance}},
  author = {Jingxuan Wei, Xingyue Wang, Yanghaoyu Liao, Jie Dong, Yuchen Liu, Caijun Jia, Bihui Yu and Junnan Zhu},
  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.228},
  url = {https://doi.org/10.18653/v1/2026.acl-long.228}
}
```

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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/31/)

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*Updated 1 November 2026*