---
title: "LoGU: Long-form Generation with Uncertainty Expressions (2025)"
description: "The authors define this as the LoGU task (Long-form Generation with Uncertainty). 42 references, cited by 13 papers here."
authors: ["Ruihan Yang", "Caiqi Zhang", "Zhisong Zhang", "Xinting Huang", "Sen Yang", "Nigel Collier", "Dong Yu", "Deqing Yang"]
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.928/"
doi: "10.18653/v1/2025.acl-long.928"
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/yang-2025-logu-long-form-generation-uncertainty/"
---
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# LoGU: Long-form Generation with Uncertainty Expressions

**Authors:** [Ruihan Yang](https://md.research.elamur.ai/free/en/authors/ruihan-yang/), [Caiqi Zhang](https://md.research.elamur.ai/free/en/authors/caiqi-zhang/), [Zhisong Zhang](https://md.research.elamur.ai/free/en/authors/zhisong-zhang/), [Xinting Huang](https://md.research.elamur.ai/free/en/authors/xinting-huang/), [Sen Yang](https://md.research.elamur.ai/free/en/authors/sen-yang/), [Nigel Collier](https://md.research.elamur.ai/free/en/authors/nigel-collier/), [Dong Yu](https://md.research.elamur.ai/free/en/authors/dong-yu/) and [Deqing Yang](https://md.research.elamur.ai/free/en/authors/deqing-yang/) · **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.928/) · [PDF](https://aclanthology.org/2025.acl-long.928.pdf) · [DOI 10.18653/v1/2025.acl-long.928](https://doi.org/10.18653/v1/2025.acl-long.928) · [OpenAlex](https://openalex.org/W4412945020)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [13](https://md.research.elamur.ai/free/en/papers/yang-2025-logu-long-form-generation-uncertainty/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Work on getting LLMs to admit what they do not know has mostly targeted short-form QA, where one confidence signal covers a single answer. Long responses mix true and false claims, so uncertainty must be expressed claim by claim. The authors define this as the LoGU task (Long-form Generation with Uncertainty). They identify two failure modes: Uncertainty Suppression, where models avoid hedging, and Uncertainty Misalignment, where hedges land on the wrong facts.

To build training data, model outputs are split into atomic claims and fact-checked with FActScore against retrieved evidence. An auxiliary LLM (GPT-4) then rewrites selected claims as uncertainty statements and merges everything back into fluent text. Training happens in two stages. LoGU-SFT teaches the model to hedge at all. LoGU-DPO then uses preference pairs to steer hedging toward facts the model actually gets wrong.

On three long-form benchmarks, the method raises factual accuracy and reduces false statements while keeping responses comprehensive. Its hedges are also more specific than the vague caveats produced by baselines. This makes long answers more trustworthy and their gaps easier to see.

## Key points
- Two metrics are defined:
  - **Factual Accuracy (FA)** is the share of supported claims among all definite claims.
  - **Uncertain Accuracy (UA)** is the share of uncertainty expressions that target genuinely unknown, fine-grained details.
- Revision uses two operators:
  - The positive operator (Γ_pos) turns refuted claims into hedges.
  - The negative operator (Γ_neg) wrongly hedges supported claims instead.
  - Rewriting draws on ten hand-crafted uncertainty patterns, with down-sampling to limit how often hedges appear.
- LoGU-DPO prefers the positive revision R_pos over both the original response and R_neg, and prefers the original over R_neg.
- Evaluation covers three datasets without gold answers: Bios, LongFact and WildHallu. For this reason, ROUGE-L and recall are not reported.
- In human evaluation, outputs with about 20% hedged content were judged most helpful, with only slightly lower fluency.
- Uncertainty is expressed as binary rather than graded. Code is available at github.com/rhyang2021/LoGU.

*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

- [University of Cambridge](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) — University, Cambridge, United Kingdom · 243 works · 121,396 citations
- [Fudan University](https://md.research.elamur.ai/free/en/universities/fudan-university/) — University, Shanghai, China · 167 works · 20,987 citations
- [Chinese University of Hong Kong](https://md.research.elamur.ai/free/en/universities/chinese-university-of-hong-kong/) — University, Hong Kong, China · 126 works · 14,073 citations

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## Cite

Cite as: Ruihan Yang, Caiqi Zhang, Zhisong Zhang, Xinting Huang, Sen Yang, Nigel Collier, Dong Yu and Deqing Yang. 2025. LoGU: Long-form Generation with Uncertainty Expressions. 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{yang2025logu,
  title = {{LoGU: Long-form Generation with Uncertainty Expressions}},
  author = {Ruihan Yang, Caiqi Zhang, Zhisong Zhang, Xinting Huang, Sen Yang, Nigel Collier, Dong Yu and Deqing Yang},
  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.928},
  url = {https://doi.org/10.18653/v1/2025.acl-long.928}
}
```

- [← previous Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/zhang-2025-less-but-better-efficient-multilingual/)
- [next → Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/yu-2025-long-short-chain-thought-mixture/)
- [↑ back to papers of 2025](https://md.research.elamur.ai/free/en/papers/2025/page/7/)

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*Updated 30 October 2026*