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ReFACT: A Benchmark for Scientific Confabulation Detection with Positional Error Annotations

Paper · 2026 · Yindong Wang, Martin Preiß, Margarita Bugueño et al.

Summary

LLMs often produce scientific text that reads fluently and sounds credible but contains factual mistakes that only a domain expert would notice. The authors call this "scientific confabulation." To measure whether models can catch such errors, they built ReFACT. It contains 1,001 question–answer pairs from r/AskScience covering 10 scientific domains. Highly rated human answers were deliberately corrupted, either by negating a claim or by swapping in a different technical entity. Gemma-2-27B-it generated the corruptions, and an NLI classifier removed swaps that were merely synonyms. Human annotators then checked every sample and marked the exact error span.

The benchmark has three tasks: deciding whether an answer is wrong, locating the erroneous span, and restoring the original content. Across 9 LLMs, the most common failure was flagging prominent but irrelevant text instead of the real error. This pattern held from 1B to 70B parameters. Models also did worse when comparing two answers than when judging one alone. These results raise doubts about relying on LLM-as-Judge setups to assess scientific factuality.

Key points

  • Dataset construction: The 1,001 samples include 527 negations and 474 entity replacements. They were drawn from 10,282 filtered r/AskScience pairs, kept only if the question scored ≥ 4 and the answer was 500–1,000 characters long. Annotation was done in doccano.
  • Annotation quality: About 57–58% of generated corruptions passed human validation. Pairwise agreement among three annotators on 100 shared samples was 72.56%.
  • Salient distractor failure: Of 5,826 false-positive span predictions, 61% pointed to text unrelated to the real error. In 90.6% of those cases, the predicted span shared no characters with the actual error.
  • Comparative judgment is harder: GPT-4o's F1 fell from 0.67 when judging answers independently to 0.53 when comparing them side by side. Its comparative accuracy was only 0.60.
  • Localization and correction: Entity swaps were harder to locate than negations. GPT-4o scored 0.47 accuracy / 0.38 IoU on entity swaps versus 0.66 / 0.57 on negations. Correction exact-match accuracy was low: 0.28 for GPT-4o, 0.44 for Gemma-3-27B, and 0.03–0.05 for 1B models.

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 PotsdamPotsdam · Germany44 works · 5,305 citations🔬Hasso Plattner InstitutePotsdam · Germany12 works · 4,998 citations

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSafety Research · 543 worksSociology and Political Science · 577 works

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 1,220 works

Cite

Cite as Yindong Wang, Martin Preiß, Margarita Bugueño, Jan Vincent Hoffbauer, Abdullatif Ghajar, Tolga Buz and Gerard de Melo. 2026. ReFACT: A Benchmark for Scientific Confabulation Detection with Positional Error Annotations. Proceedings of the 19th Conference of the European Chapter 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. DOI 10.18653/v1/2026.eacl-long.381

@inproceedings{wang2026refact,
  title = {{ReFACT: A Benchmark for Scientific Confabulation Detection with Positional Error Annotations}},
  author = {Yindong Wang, Martin Preiß, Margarita Bugueño, Jan Vincent Hoffbauer, Abdullatif Ghajar, Tolga Buz and Gerard {de Melo}},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.381},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.381}
}

References

32 references, 14 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty, Giovanni Luca Ciampaglia, David Corney, Renee DiResta, Emilio Ferrara, Scott Hale, Alon Halevy, Eduard Hovy, Heng Ji, Filippo Menczer, Ruben Miguez, Preslav Nakov, Dietram Scheufele, Shivam Sharma, and Giovanni Zagni. 2023. Fact… — Factuality Challenges in the Era of Large Language Models
  2. Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, and Pascale Fung. 2025. HalluLens: LLM hallucination benchmark. Preprint, arXiv:2504.17550.
  3. Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural language processing with Python: Analyzing text with the natural language toolkit. O’Reilly Media, Inc. find
  4. Canyu Chen and Kai Shu. 2023. Combating misinformation in the age of LLMs: Opportunities and challenges. Preprint, arXiv:2311.05656. find
  5. Shiqi Chen, Yiran Zhao, Jinghan Zhang, I-Chun Chern, Siyang Gao, Pengfei Liu, and Junxian He. 2023. find
  6. FELM: Benchmarking factuality evaluation of large language models. Preprint, arXiv:2310.00741. find
  7. Roi Cohen, Konstantin Dobler, Eden Biran, and Gerard de Melo. 2024. I don’t know: Explicit modeling of uncertainty with an [IDK] token. In Proceedings of 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024). find
  8. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, and 542 o… — The Llama 3 Herd of Models
  9. Jian Guan, Jesse Dodge, David Wadden, Minlie Huang, and Hao Peng. 2024. Language models hallucinate, but may excel at fact verification. Preprint, arXiv:2310.14564. find
  10. Jan Hoffbauer, Sylwester Sawicki, Marc Ulrich, Tolga Buz, Konstantin Dobler, Moritz Schneider, and Gerard De Melo. 2024. Knowledge acquisition through continued pretraining is difficult: A case study on r/AskHistorians. In Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (Kno… find
  11. Beizhe Hu, Qiang Sheng, Juan Cao, Yuhui Shi, Yang Li, Danding Wang, and Peng Qi. 2024. Bad actor, good advisor: Exploring the role of large language models in fake news detection. Proceedings of the AAAI Conference on Artificial Intelligence, 38:22105– 22113. find
  12. Yue Huang and Lichao Sun. 2024. FakeGPT: Fake news generation, explanation and detection of large language models. Preprint, arXiv:2310.05046. — [arXiv:2310.05046](https://arxiv.org/abs/2310.05046) find
  13. Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. ACM computing surveys, 55(12):1–38.
  14. Moritz Laurer, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI. Preprint. Publisher: Open Science Framework. find
  15. Nayeon Lee, Wei Ping, Peng Xu, Mostofa Patwary, Pascale Fung, Mohammad Shoeybi, and Bryan Catanzaro. 2023. Factuality enhanced language models for open-ended text generation. Preprint, arXiv:2206.04624. find
  16. Junyi Li, Jie Chen, Ruiyang Ren, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2024a. The dawn after the dark: An empirical study on factuality hallucination in large language models. Preprint, arXiv:2401.03205.
  17. Junyi Li, Xiaoxue Cheng, Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen. 2023. HaluEval: A large-scale hallucination evaluation benchmark for large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6449–6464, Singapore. Association for Computa…
  18. Xinyi Li, Yongfeng Zhang, and Edward C. Malthouse. 2024b. Large language model agent for fake news detection. Preprint, arXiv:2405.01593. — [arXiv:2405.01593](https://arxiv.org/abs/2405.01593) find
  19. Stephanie Lin, Jacob Hilton, and Owain Evans. 2022. TruthfulQA: Measuring how models mimic human falsehoods. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3214–3252, Dublin, Ireland. Association for Computational Linguistics…
  20. Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. On faithfulness and factuality in abstractive summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), pages 1906–1919.
  21. Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023. FActScore: Fine-grained atomic evaluation of factual precision in long form text generation. Preprint, arXiv:2305.14251.
  22. Abhika Mishra, Akari Asai, Vidhisha Balachandran, Yizhong Wang, Graham Neubig, Yulia Tsvetkov, and Hannaneh Hajishirzi. 2024. Fine-grained hallucination detection and editing for language models. Preprint, arXiv:2401.06855.
  23. Hiroki Nakayama, Takahiro Kubo, Junya Kamura, Yasufumi Taniguchi, and Xu Liang. 2018. doccano: Text annotation tool for human. Software available from https://github.com/doccano/doccano. ↗
  24. OpenAI. 2024. GPT-4 technical report. arXiv:2303.08774.
  25. Preprint, Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383–2392, Austin, Texas. Association for Computational Lin…
  26. Abhilasha Ravichander, Shrusti Ghela, David Wadden, and Yejin Choi. 2025. HALoGEN: Fantastic LLM hallucinations and where to find them. Preprint, arXiv:2501.08292.
  27. Peiqi Sui, Eamon Duede, Sophie Wu, and Richard So. 2024a. Confabulation: The surprising value of large language model hallucinations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14274–14284, Bangkok, Thailand. Association… find
  28. Peiqi Sui, Eamon Duede, Sophie Wu, and Richard Jean So. 2024b. Confabulation: The surprising value of large language model hallucinations. Preprint, arXiv:2406.04175. find
  29. Gemma Team, Aishwarya Kamath, Johan Ferret, Shreya Pathak, Nino Vieillard, Ramona Merhej, Sarah Perrin, Tatiana Matejovicova, Alexandre Ramé, Morgane Rivière, Louis Rouillard, Thomas Mesnard, Geoffrey Cideron, Jean bastien Grill, Sabela Ramos, Edouard Yvinec, Michelle Casbon, Etienne Pot, Ivo Penche… find
  30. Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, Peter Liu, Pouya Tafti, Abe Friesen, Michelle Casbon, Sabela Ramos, Ravin Kumar, Charline Le Lan, Sammy Jerome, and 179… find
  31. Weixing Wang, Zifeng Ding, Jindong Gu, Rui Cao, Christoph Meinel, Gerard de Melo, and Haojin Yang. 2025. Image tokens matter: Mitigating hallucination in discrete tokenizer-based large vision-language models via latent editing. In Advances in Neural Information Processing Systems (NeurIPS) 2025. find
  32. Jason Wei, Karina Nguyen, Hyung Won Chung, Yunxin Joy Jiao, Spencer Papay, Amelia Glaese, John Schulman, and William Fedus. 2024. Measuring short-form factuality in large language models. arXiv preprint arXiv:2411.04368.

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