---
title: "Explaining Sources of Uncertainty in Automated Fact-Checking"
description: "This paper introduces CLUE (Conflict-&Agreement-aware Language-model Uncertainty Explanations)… 76 references, cited by 2 papers here."
authors: ["Jingyi Sun", "Greta Warren", "Irina Shklovski", "Isabelle Augenstein"]
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.2110/"
doi: "10.18653/v1/2026.acl-long.2110"
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/sun-2026-explaining-sources-uncertainty-automated-fact/"
---
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# Explaining Sources of Uncertainty in Automated Fact-Checking

**Authors:** Jingyi Sun, Greta Warren, Irina Shklovski and [Isabelle Augenstein](https://md.research.elamur.ai/free/en/authors/isabelle-augenstein/) · **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.2110/) · [PDF](https://aclanthology.org/2026.acl-long.2110.pdf) · [DOI 10.18653/v1/2026.acl-long.2110](https://doi.org/10.18653/v1/2026.acl-long.2110) · [OpenAlex](https://openalex.org/W7166797198)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [2](https://md.research.elamur.ai/free/en/papers/sun-2026-explaining-sources-uncertainty-automated-fact/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary

Fact-checkers working with LLMs need to know *why* a model is unsure, not just *how* unsure it is. Numeric confidence scores and verbal hedges do not show which pieces of evidence clash. This paper introduces CLUE (Conflict-&Agreement-aware Language-model Uncertainty Explanations), a white-box, plug-and-play pipeline that runs only at inference time. It needs no fine-tuning or gold explanations. CLUE works in three steps:

1. It measures uncertainty as the entropy of the model's label distribution.
2. It finds important span-level interactions between the claim and each evidence passage, and between the passages, using attention without supervision. It then labels each interaction as agreement, conflict, or unrelated.
3. It feeds these spans to the model as soft guidance so that the generated explanation ties the uncertainty to specific conflicts and agreements.

The authors tested CLUE on two fact-checking datasets and several open models, comparing it with plain explanation prompting. CLUE's explanations tracked model uncertainty more faithfully and matched the fact-checking decisions more closely. Human raters also found them more helpful and informative. The work is a step toward fact-checking tools that point users to the specific evidence tensions worth verifying.

## Key points

- **Data and models:** Experiments use the HealthVer and DRUID datasets, with one claim and two or three evidence passages per instance. Labels are SUPPORTS, REFUTES and NEUTRAL. The models include Qwen2.5-14B-Instruct, Gemma2-9B-It and OLMo-2-1124-13B-Instruct, and the limitations section also lists DeepSeek-R1-Distill-Qwen-14B.
- **Relation labelling:** GPT-4o assigns AGREE, DISAGREE or UNRELATED to each span interaction in the main experiments. A fully open-weight labeller based on Qwen2.5-14B-Instruct gives comparable explanation quality, with only a slight decrease.
- **Trade-off between variants:** Two variants of CLUE were compared. One scores better on faithfulness and the other on plausibility.
- **Human study:** Participants were native English speakers paid about 9 GBP/hour. They rated CLUE explanations as less redundant and more logically consistent than the baseline.
- **Limitations:** The study covers English only and medium-sized models. It addresses only uncertainty caused by evidence conflicts, not uncertainty from missing evidence or gaps in the model's knowledge. Errors from earlier pipeline steps can carry through, such as fragmentary spans or wrong relation labels.

*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: Jingyi Sun, Greta Warren, Irina Shklovski and Isabelle Augenstein. 2026. Explaining Sources of Uncertainty in Automated Fact-Checking. 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{sun2026explaining,
  title = {{Explaining Sources of Uncertainty in Automated Fact-Checking}},
  author = {Jingyi Sun, Greta Warren, Irina Shklovski and Isabelle Augenstein},
  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.2110},
  url = {https://doi.org/10.18653/v1/2026.acl-long.2110}
}
```

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*Updated 2 November 2026*