---
title: "TROVE — paper by Zhu et al. (2025) — summary and ideas"
description: "TROVE frames text provenance as a fine-grained task. 36 references, cited by 3 papers here."
authors: ["Junnan Zhu", "Min Xiao", "Yining Wang", "Feifei Zhai", "Yu Zhou", "Chengqing Zong"]
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.577/"
doi: "10.18653/v1/2025.acl-long.577"
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/zhu-2025-trove-challenge-fine-grained-text/"
---
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# TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification

**Authors:** [Junnan Zhu](https://md.research.elamur.ai/free/en/authors/junnan-zhu/), Min Xiao, [Yining Wang](https://md.research.elamur.ai/free/en/authors/yining-wang/), [Feifei Zhai](https://md.research.elamur.ai/free/en/authors/feifei-zhai/), [Yu Zhou](https://md.research.elamur.ai/free/en/authors/yu-zhou/) and [Chengqing Zong](https://md.research.elamur.ai/free/en/authors/chengqing-zong/) · **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.577/) · [PDF](https://aclanthology.org/2025.acl-long.577.pdf) · [DOI 10.18653/v1/2025.acl-long.577](https://doi.org/10.18653/v1/2025.acl-long.577) · [OpenAlex](https://openalex.org/W4412945371)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [3](https://md.research.elamur.ai/free/en/papers/zhu-2025-trove-challenge-fine-grained-text/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
TROVE frames text provenance as a fine-grained task. For every sentence of a generated or target text, a system must find the exact source sentences it came from, even when the input is long or split across several documents. It must also label how each sentence was derived: quotation, compression, inference, or other (e.g., negation). This differs from citation generation, fact verification and grounded generation, which usually work at document level or with coarser links. The authors built a benchmark from LongBench, LooGLE and CRUD-RAG. It covers 11 scenarios such as QA and summarization, English and Chinese, and inputs from under 5k to over 10k in length. Annotation ran in three steps: candidate retrieval, a first labeling pass by GPT-4o, and human verification. They tested 11 LLMs with direct prompting (DP) and retrieval-augmented (RA) setups. Retrieval helped every model. Bigger models classified relationships more accurately. Closed-source systems scored highest, but open-source models with retrieval came close. Relationship labeling remains hard. The resource supports accountability for generated text in sensitive fields such as law and medicine.

## Key points
- The dataset draws on LongBench, LooGLE and CRUD-RAG and spans 11 scenarios, 2 languages and 3 source-length bands. It is released at github.com/ZNLP/ZNLP-Dataset.
- One target sentence can map to multiple source sentences, and each link carries its own label. Labels can be mixed, for example compression for two sources and quotation for a third.
- Gemini-1.5-Pro and GPT-4o achieved the best F1. Retrieval-augmented Qwen2.5-14B sometimes matched them and outperformed Kimi and Gemini-1.5-Pro once sources exceeded 20k.
- In GPT-4o's errors, quotation was the easiest label and inference the hardest. Under RA, 311 of 672 true inference cases were labeled compression, and only 7 of 28 "other" cases were correct. Quotation→compression errors increased from 401 (DP) to 533 (RA).
- Limitation: the data comes from existing human-written datasets rather than LLM output, so it contains no hallucination cases.

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

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## Cite

Cite as: Junnan Zhu, Min Xiao, Yining Wang, Feifei Zhai, Yu Zhou and Chengqing Zong. 2025. TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification. 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{zhu2025trove,
  title = {{TROVE: A Challenge for Fine-Grained Text Provenance via Source Sentence Tracing and Relationship Classification}},
  author = {Junnan Zhu, Min Xiao, Yining Wang, Feifei Zhai, Yu Zhou and Chengqing Zong},
  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.577},
  url = {https://doi.org/10.18653/v1/2025.acl-long.577}
}
```

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## References

36 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*