---
title: "Pushing the Frontiers of Scientific Fact-Checking: The SCINLP"
description: "This paper presents SCINLP, the first fact-checking resource built specifically for NLP research. 0.7898 accuracy. 58 references."
authors: ["Iffat Maab", "Junichi Yamagishi"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.81/"
doi: "10.18653/v1/2026.findings-eacl.81"
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/maab-2026-pushing-frontiers-scientific-fact-checking/"
---
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# Pushing the Frontiers of Scientific Fact-Checking: The SCINLP Dataset

**Authors:** [Iffat Maab](https://md.research.elamur.ai/free/en/authors/iffat-maab/) and [Junichi Yamagishi](https://md.research.elamur.ai/free/en/authors/junichi-yamagishi/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/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.findings-eacl.81/) · [PDF](https://aclanthology.org/2026.findings-eacl.81.pdf) · [DOI 10.18653/v1/2026.findings-eacl.81](https://doi.org/10.18653/v1/2026.findings-eacl.81) · [OpenAlex](https://openalex.org/W7140129523)

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

## Summary
Checking scientific claims is difficult because it requires domain expertise, and existing benchmarks focus mostly on biomedical and health topics. This paper presents SCINLP, the first fact-checking resource built specifically for NLP research. The authors sampled ACL Anthology papers at three-year intervals from 2000 to 2024 and used GPT-4 to extract self-contained supporting and refuting claims. They retrieved evidence passages from the source papers with Facebook/Contriever and added metadata as optional rationale context. Open-source LLMs were then tested zero-shot, without fine-tuning, on three tasks: predicting veracity, explaining their verdicts, and placing each claim in time as Advanced, Outdated, or Still Holds True. Two findings stand out. Accuracy rises as more article context is supplied, and rewriting claims to be self-contained (decontextualization) improves results across model sizes. Gemma-27B was the most robust model. Because it uses real claims rather than synthetic ones, SCINLP offers a realistic benchmark for checking the reliability of NLP literature.

## Key points
- SCINLP contains 7,033 claims, both supporting and refuting, taken from ACL Anthology papers published between 2000 and 2024. It covers topics such as machine translation, neural models, transformers, and LLMs.
- Six rationale context types were tested: title, author(s), year, abstract, evidence, and keywords. The experiments used three open-source LLM families at roughly 8B and 70B scale, giving 24 settings in total.
- The authors evaluated 3,615 decontextualized claims, which is about half of the dataset because of budget limits.
- Gemma-27B performed best:
  - With decontextualized claims plus full context, it reached 0.7898 accuracy and 0.7878 F1.
  - In the claim-only setting, decontextualization raised its accuracy from 0.7119 to 0.7406.
  - It was more robust than both Qwen-72B and Llama-70B.
- The study has several limitations:
  - The temporal labels have no ground truth for comparison.
  - Retrieving evidence from multiple sources via Serper gave only small gains.
  - The data is English-only.

*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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- [National Institute of Informatics](https://md.research.elamur.ai/free/en/labs/national-institute-of-informatics/) — Research institute, Tokyo, Japan · 37 works · 3,247 citations

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## Cite

Cite as: Iffat Maab and Junichi Yamagishi. 2026. Pushing the Frontiers of Scientific Fact-Checking: The SCINLP Dataset. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{maab2026pushing,
  title = {{Pushing the Frontiers of Scientific Fact-Checking: The SCINLP Dataset}},
  author = {Iffat Maab and Junichi Yamagishi},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.81},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.81}
}
```

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- [next → Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering](https://md.research.elamur.ai/free/en/papers/wang-2026-putting-captions-test-evaluating-video/)
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## References

58 references, 27 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Anirudh Ajith, Mengzhou Xia, Alexis Chevalier, Tanya Goyal, Danqi Chen, and Tianyu Gao. 2024. [Litsearch: A retrieval benchmark for scientific literature search](https://md.research.elamur.ai/free/en/papers/ajith-2024-litsearch-retrieval-benchmark-scientific-literature/). arXiv preprint arXiv:2407.18940.
2. Enes Altuncu, Can Baskent, Sanjay Bhattacherjee, Shujun Li, and Dwaipayan Roy. 2025. Factors: A new dataset for studying the fact-checking ecosystem. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 3530–3539. [find](https://scholar.google.com/scholar?q=Enes+Altuncu%2C+Can+Baskent%2C+Sanjay+Bhattacherjee%2C+Shujun+Li%2C+and+Dwaipayan+Roy%2E+2025%2E+Factors%3A+A+new+dataset+for+studying+the+fact%2Dchecking+ecosystem%2E+In+Proceedings+of+the+48th+International+ACM+SIGIR)
3. Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, and Isabelle Augenstein. 2022. [Fact checking with insufficient evidence](https://md.research.elamur.ai/free/en/papers/atanasova-2022-fact-checking-insufficient-evidence/). Transactions of the Association for Computational Linguistics, 10:746–763.
4. Tim Baumgärtner, Ted Briscoe, and Iryna Gurevych. 2025. Peerqa: A scientific question answering dataset from peer reviews. arXiv preprint arXiv:2502.13668. [find](https://scholar.google.com/scholar?q=Tim+Baumg%C3%A4rtner%2C+Ted+Briscoe%2C+and+Iryna+Gurevych%2E+2025%2E+Peerqa%3A+A+scientific+question+answering+dataset+from+peer+reviews%2E+arXiv+preprint+arXiv%3A2502%2E13668%2E)
5. Lutz Bornmann, Robin Haunschild, and Rüdiger Mutz. 2021. Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases. Humanities and Social Sciences Communications, 8(1):1– 15. [find](https://scholar.google.com/scholar?q=Lutz+Bornmann%2C+Robin+Haunschild%2C+and+R%C3%BCdiger+Mutz%2E+2021%2E+Growth+rates+of+modern+science%3A+a+latent+piecewise+growth+curve+approach+to+model+publication+numbers+from+established+and+new+literature+datab)
6. Erik Cambria and Bebo White. 2014. Jumping nlp curves: A review of natural language processing research. IEEE Computational intelligence magazine, 9(2):48–57. [find](https://scholar.google.com/scholar?q=Erik+Cambria+and+Bebo+White%2E+2014%2E+Jumping+nlp+curves%3A+A+review+of+natural+language+processing+research%2E+IEEE+Computational+intelligence+magazine%2C+9%282%29%3A48%E2%80%9357%2E)
7. Katherine M Collins, Albert Q Jiang, Simon Frieder, Lionel Wong, Miri Zilka, Umang Bhatt, Thomas Lukasiewicz, Yuhuai Wu, Joshua B Tenenbaum, William Hart, and 1 others. 2024. Evaluating language models for mathematics through interactions. Proceedings of the National Academy of Sciences, 121(24):e23… [find](https://scholar.google.com/scholar?q=Katherine+M+Collins%2C+Albert+Q+Jiang%2C+Simon+Frieder%2C+Lionel+Wong%2C+Miri+Zilka%2C+Umang+Bhatt%2C+Thomas+Lukasiewicz%2C+Yuhuai+Wu%2C+Joshua+B+Tenenbaum%2C+William+Hart%2C+and+1+others%2E+2024%2E+Evaluating+language+model)
8. Cristina Cornelio, Sanjeeb Dash, Vernon Austel, Tyler R Josephson, Joao Goncalves, Kenneth L Clarkson, Nimrod Megiddo, Bachir El Khadir, and Lior Horesh. 2023. Combining data and theory for derivable scientific discovery with ai-descartes. Nature Communications, 14(1):1777. [find](https://scholar.google.com/scholar?q=Cristina+Cornelio%2C+Sanjeeb+Dash%2C+Vernon+Austel%2C+Tyler+R+Josephson%2C+Joao+Goncalves%2C+Kenneth+L+Clarkson%2C+Nimrod+Megiddo%2C+Bachir+El+Khadir%2C+and+Lior+Horesh%2E+2023%2E+Combining+data+and+theory+for+derivable+)
9. Xingyu Deng, Xi Wang, and Mark Stevenson. 2025. The next phase of scientific fact-checking: advanced evidence retrieval from complex structured academic papers. In Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR), pages 4… [find](https://scholar.google.com/scholar?q=Xingyu+Deng%2C+Xi+Wang%2C+and+Mark+Stevenson%2E+2025%2E+The+next+phase+of+scientific+fact%2Dchecking%3A+advanced+evidence+retrieval+from+complex+structured+academic+papers%2E+In+Proceedings+of+the+2025+Internationa)
10. Thomas Diggelmann, Jordan Boyd-Graber, Jannis Bulian, Massimiliano Ciaramita, and Markus Leippold. 2020. [Climate-fever: A dataset for verification of real-world climate claims](https://md.research.elamur.ai/free/en/papers/diggelmann-2020-climate-fever-dataset-verification-real/). arXiv preprint arXiv:2012.00614.
11. Islam Eldifrawi, Shengrui Wang, and Amine Trabelsi. 2024. Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approaches. Preprint, arXiv:2407.12853. [find](https://scholar.google.com/scholar?q=Islam+Eldifrawi%2C+Shengrui+Wang%2C+and+Amine+Trabelsi%2E+2024%2E+Automated+Justification+Production+for+Claim+Veracity+in+Fact+Checking%3A+A+Survey+on+Architectures+and+Approaches%2E+Preprint%2C+arXiv%3A2407%2E12853%2E)
12. Hugging Face. 2021. The ai community building the future. URL: [https://huggingface.co](https://huggingface.co). [↗](https://huggingface.co)
13. Michael Fire and Carlos Guestrin. 2019. Overoptimization of academic publishing metrics: observing goodhart’s law in action. GigaScience, 8(6):giz053. [find](https://scholar.google.com/scholar?q=Michael+Fire+and+Carlos+Guestrin%2E+2019%2E+Overoptimization+of+academic+publishing+metrics%3A+observing+goodhart%E2%80%99s+law+in+action%2E+GigaScience%2C+8%286%29%3Agiz053%2E)
14. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv preprint arXiv:2407.21783.
15. Max Grusky. 2023. Rogue scores. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1914–1934. [find](https://scholar.google.com/scholar?q=Max+Grusky%2E+2023%2E+Rogue+scores%2E+In+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Computational+Linguistics+%28Volume+1%3A+Long+Papers%29%2C+pages+1914%E2%80%931934%2E)
16. Anisha Gunjal and Greg Durrett. 2024. Molecular facts: Desiderata for decontextualization in LLM fact verification. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 3751–3768, Miami, Florida, USA. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Anisha+Gunjal+and+Greg+Durrett%2E+2024%2E+Molecular+facts%3A+Desiderata+for+decontextualization+in+LLM+fact+verification%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2024%2C+pages+3751)
17. Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018. [Annotation artifacts in natural language inference data](https://md.research.elamur.ai/free/en/papers/gururangan-2018-annotation-artifacts-natural-language-inference/). In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techno…
18. Andreas Hanselowski, Christian Stab, Claudia Schulz, Zile Li, and Iryna Gurevych. 2019. [A richly annotated corpus for different tasks in automated fact-checking](https://md.research.elamur.ai/free/en/papers/hanselowski-2019-richly-annotated-corpus-different-tasks/). In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 493–503, Hong Kong, China. Association for…
19. Andreas Hanselowski, Hao Zhang, Zile Li, Daniil Sorokin, Benjamin Schiller, Claudia Schulz, and Iryna Gurevych. 2018. [Ukp-athene: Multi-sentence textual entailment for claim verification](https://md.research.elamur.ai/free/en/papers/hanselowski-2018-ukp-athene-multi-sentence-textual/). arXiv preprint arXiv:1809.01479.
20. Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2023. [Atlas: Few-shot learning with retrieval augmented language models](https://md.research.elamur.ai/free/en/papers/izacard-2022-atlas-few-shot-learning-retrieval/). Journal of Machine Learning Research, 24(251):1–43.
21. Richard AA Jonker, Tiago Almeida, and Sérgio Matos. 2024. Analyzing a decade of evolution: Trends in natural language processing. In International Conference on Big Data Analytics and Knowledge Discovery, pages 162–176. Springer. [find](https://scholar.google.com/scholar?q=Richard+AA+Jonker%2C+Tiago+Almeida%2C+and+S%C3%A9rgio+Matos%2E+2024%2E+Analyzing+a+decade+of+evolution%3A+Trends+in+natural+language+processing%2E+In+International+Conference+on+Big+Data+Analytics+and+Knowledge+Discov)
22. Mael Jullien, Marco Valentino, Hannah Frost, Paul O’Regan, Dónal Landers, and Andre Freitas. 2023. NLI4CT: Multi-evidence natural language inference for clinical trial reports. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16745–16764, Singapore. As… [find](https://scholar.google.com/scholar?q=Mael+Jullien%2C+Marco+Valentino%2C+Hannah+Frost%2C+Paul+O%E2%80%99Regan%2C+D%C3%B3nal+Landers%2C+and+Andre+Freitas%2E+2023%2E+NLI4CT%3A+Multi%2Devidence+natural+language+inference+for+clinical+trial+reports%2E+In+Proceedings+of+the+2)
23. Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, and Roy Schwartz. 2018. [A dataset of peer reviews (PeerRead): Collection, insights and NLP applications](https://md.research.elamur.ai/free/en/papers/kang-2018-dataset-peer-reviews-peerread-collection/). In Proceedings of the 2018 Conference of the North American Chapter of the Association for Comp…
24. Hiroaki Kitano. 2021. Nobel turing challenge: creating the engine for scientific discovery. NPJ systems biology and applications, 7(1):29. [find](https://scholar.google.com/scholar?q=Hiroaki+Kitano%2E+2021%2E+Nobel+turing+challenge%3A+creating+the+engine+for+scientific+discovery%2E+NPJ+systems+biology+and+applications%2C+7%281%29%3A29%2E)
25. Neema Kotonya and Francesca Toni. 2020. [Explainable automated fact-checking for public health claims](https://md.research.elamur.ai/free/en/papers/kotonya-2020-explainable-automated-fact-checking-public/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7740–7754, Online. Association for Computational Linguistics.
26. Michelle S Lam, Janice Teoh, James A Landay, Jeffrey Heer, and Michael S Bernstein. 2024. Concept induction: Analyzing unstructured text with high-level concepts using lloom. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, pages 1–28. [find](https://scholar.google.com/scholar?q=Michelle+S+Lam%2C+Janice+Teoh%2C+James+A+Landay%2C+Jeffrey+Heer%2C+and+Michael+S+Bernstein%2E+2024%2E+Concept+induction%3A+Analyzing+unstructured+text+with+high%2Dlevel+concepts+using+lloom%2E+In+Proceedings+of+the+202)
27. Steven A Lehr, Aylin Caliskan, Suneragiri Liyanage, and Mahzarin R Banaji. 2024. Chatgpt as research scientist: Probing gpt’s capabilities as a research librarian, research ethicist, data generator, and data predictor. Proceedings of the National Academy of Sciences, 121(35):e2404328121. [find](https://scholar.google.com/scholar?q=Steven+A+Lehr%2C+Aylin+Caliskan%2C+Suneragiri+Liyanage%2C+and+Mahzarin+R+Banaji%2E+2024%2E+Chatgpt+as+research+scientist%3A+Probing+gpt%E2%80%99s+capabilities+as+a+research+librarian%2C+research+ethicist%2C+data+generator%2C+a)
28. Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. 2024. [The ai scientist: Towards fully automated open-ended scientific discovery](https://md.research.elamur.ai/free/en/papers/lu-2024-ai-scientist-fully-automated/). arXiv preprint arXiv:2408.06292.
29. Exploring task formulation and assessment with llms. arXiv preprint arXiv:2502.08909. — [arXiv:2502.08909](https://arxiv.org/abs/2502.08909) [find](https://scholar.google.com/scholar?q=Exploring+task+formulation+and+assessment+with+llms%2E+arXiv+preprint+arXiv%3A2502%2E08909%2E+%E2%80%94+%5BarXiv%3A2502%2E08909%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2502%2E08909%29)
30. 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](https://md.research.elamur.ai/free/en/papers/mishra-2024-fine-grained-hallucination-detection-editing/). arXiv preprint arXiv:2401.06855.
31. Chris Samarinas, Wynne Hsu, and Mong-Li Lee. 2021. Improving evidence retrieval for automated explainable fact-checking. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations, pages 84–91. [find](https://scholar.google.com/scholar?q=Chris+Samarinas%2C+Wynne+Hsu%2C+and+Mong%2DLi+Lee%2E+2021%2E+Improving+evidence+retrieval+for+automated+explainable+fact%2Dchecking%2E+In+Proceedings+of+the+2021+Conference+of+the+North+American+Chapter+of+the+Asso)
32. Isabelle Mohr, Amelie Wührl, and Roman Klinger. 2022. CoVERT: A corpus of fact-checked biomedical COVID-19 tweets. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 244–257, Marseille, France. European Language Resources Association. [find](https://scholar.google.com/scholar?q=Isabelle+Mohr%2C+Amelie+W%C3%BChrl%2C+and+Roman+Klinger%2E+2022%2E+CoVERT%3A+A+corpus+of+fact%2Dchecked+biomedical+COVID%2D19+tweets%2E+In+Proceedings+of+the+Thirteenth+Language+Resources+and+Evaluation+Conference%2C+pages+)
33. Mourad Sarrouti, Asma Ben Abacha, Yassine M’rabet, and Dina Demner-Fushman. 2021. [Evidence-based fact-checking of health-related claims](https://md.research.elamur.ai/free/en/papers/sarrouti-2021-evidence-based-fact-checking-health/). In Findings of the association for computational linguistics: EMNLP 2021, pages 3499–3512.
34. Liangming Pan, Xiaobao Wu, Xinyuan Lu, Anh Tuan Luu, William Yang Wang, Min-Yen Kan, and Preslav Nakov. 2023a. [Fact-checking complex claims with program-guided reasoning](https://md.research.elamur.ai/free/en/papers/pan-2023-fact-checking-complex-claims-program/). In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6981–70…
35. Liangming Pan, Xiaobao Wu, Xinyuan Lu, Anh Tuan Luu, William Yang Wang, Min-Yen Kan, and Preslav Nakov. 2023b. [Fact-Checking Complex Claims with Program-Guided Reasoning](https://md.research.elamur.ai/free/en/papers/pan-2023-fact-checking-complex-claims-program/). In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6981–70…
36. Ori Press, Andreas Hochlehnert, Ameya Prabhu, Vishaal Udandarao, Ofir Press, and Matthias Bethge. 2024. Citeme: Can language models accurately cite scientific claims? Advances in Neural Information Processing Systems, 37:7847–7877. [find](https://scholar.google.com/scholar?q=Ori+Press%2C+Andreas+Hochlehnert%2C+Ameya+Prabhu%2C+Vishaal+Udandarao%2C+Ofir+Press%2C+and+Matthias+Bethge%2E+2024%2E+Citeme%3A+Can+language+models+accurately+cite+scientific+claims%3F+Advances+in+Neural+Information+Pr)
37. Anna Rogers and Isabelle Augenstein. 2020. What can we do to improve peer review in nlp? arXiv preprint arXiv:2010.03863. [find](https://scholar.google.com/scholar?q=Anna+Rogers+and+Isabelle+Augenstein%2E+2020%2E+What+can+we+do+to+improve+peer+review+in+nlp%3F+arXiv+preprint+arXiv%3A2010%2E03863%2E)
38. Alexey Romanov and Chaitanya Shivade. 2018. [Lessons from natural language inference in the clinical domain](https://md.research.elamur.ai/free/en/papers/romanov-2018-lessons-natural-language-inference-clinical/). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1586–1596, Brussels, Belgium. Association for Computational Linguistics.
39. Arkadiy Saakyan, Tuhin Chakrabarty, and Smaranda Muresan. 2021. [Covid-fact: Fact extraction and verification of real-world claims on covid-19 pandemic](https://md.research.elamur.ai/free/en/papers/saakyan-2021-covid-fact-fact-extraction-verification/). arXiv preprint arXiv:2106.03794.
40. Mobashir Sadat and Cornelia Caragea. 2022. [SciNLI: A corpus for natural language inference on scientific text](https://md.research.elamur.ai/free/en/papers/sadat-2022-scinli-corpus-natural-language-inference/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7399–7409, Dublin, Ireland. Association for Computational Linguisti…
41. Premtim Sahitaj, Iffat Maab, Junichi Yamagishi, Jawan Kolanowski, Sebastian Möller, and Vera Schmitt. 2025. [Towards automated fact-checking of real-world claims: Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, Aayush Gupta, HyoJung Han, Sevi…](https://md.research.elamur.ai/free/en/papers/schulhoff-2024-prompt-report-systematic-survey-prompt/)
42. Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, Daniel Roberto Filizzola Ortiz, Enrico Santus, and Regina Barzilay. 2019. [Towards debiasing fact verification models](https://md.research.elamur.ai/free/en/papers/schuster-2019-debiasing-fact-verification-models/). In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on…
43. Chenglei Si, Diyi Yang, and Tatsunori Hashimoto. 2024. [Can llms generate novel research ideas? a large-scale human study with 100+ nlp researchers](https://md.research.elamur.ai/free/en/papers/si-2024-llms-generate-novel-research/). arXiv preprint arXiv:2409.04109.
44. Dhara Solanki, Amit Thakkar, Kush Patel, Jigar Sarda, and Akash Kumar Bhoi. 2024. A review on approaches and applications of natural language inference. In International Conference on Data Analytics & Management, pages 441–454. Springer. [find](https://scholar.google.com/scholar?q=Dhara+Solanki%2C+Amit+Thakkar%2C+Kush+Patel%2C+Jigar+Sarda%2C+and+Akash+Kumar+Bhoi%2E+2024%2E+A+review+on+approaches+and+applications+of+natural+language+inference%2E+In+International+Conference+on+Data+Analytics+%26)
45. Neset Tan, Trung Nguyen, Josh Bensemann, Alex Peng, Qiming Bao, Yang Chen, Mark Gahegan, and Michael Witbrock. 2023. Multi2Claim: Generating scientific claims from multi-choice questions for scientific fact-checking. In Proceedings of the 17th Conference of the European Chapter of the Association fo… [find](https://scholar.google.com/scholar?q=Neset+Tan%2C+Trung+Nguyen%2C+Josh+Bensemann%2C+Alex+Peng%2C+Qiming+Bao%2C+Yang+Chen%2C+Mark+Gahegan%2C+and+Michael+Witbrock%2E+2023%2E+Multi2Claim%3A+Generating+scientific+claims+from+multi%2Dchoice+questions+for+scientifi)
46. Qwen Team. 2024. Qwen2.5: A party of foundation models. [find](https://scholar.google.com/scholar?q=Qwen+Team%2E+2024%2E+Qwen2%2E5%3A+A+party+of+foundation+models%2E)
47. Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong. 2024. [Solving olympiad geometry without human demonstrations](https://md.research.elamur.ai/free/en/papers/trinh-2024-solving-olympiad-geometry-without-human/). Nature, 625(7995):476– 482.
48. Juraj Vladika and Florian Matthes. 2023. Scientific fact-checking: A survey of resources and approaches. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6215–6230, Toronto, Canada. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Juraj+Vladika+and+Florian+Matthes%2E+2023%2E+Scientific+fact%2Dchecking%3A+A+survey+of+resources+and+approaches%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+2023%2C+pages+6215%E2%80%936230%2C+Toront)
49. David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. 2020. [Fact or fiction: Verifying scientific claims](https://md.research.elamur.ai/free/en/papers/wadden-2020-fact-or-fiction-verifying-scientific/). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7534–7550, Online. Association…
50. David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Iz Beltagy, Lucy Lu Wang, and Hannaneh Hajishirzi. 2022. [Scifact-open: Towards open-domain scientific claim verification](https://md.research.elamur.ai/free/en/papers/wadden-2022-scifact-open-open-domain-scientific/). arXiv preprint arXiv:2210.13777.
51. Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, and 1 others. 2023. Scientific discovery in the age of artificial intelligence. Nature, 620(7972):47–60. [find](https://scholar.google.com/scholar?q=Hanchen+Wang%2C+Tianfan+Fu%2C+Yuanqi+Du%2C+Wenhao+Gao%2C+Kexin+Huang%2C+Ziming+Liu%2C+Payal+Chandak%2C+Shengchao+Liu%2C+Peter+Van+Katwyk%2C+Andreea+Deac%2C+and+1+others%2E+2023%2E+Scientific+discovery+in+the+age+of+artificia)
52. Haoran Wang and Kai Shu. 2023. Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6288–6304, Singapore. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Haoran+Wang+and+Kai+Shu%2E+2023%2E+Explainable+Claim+Verification+via+Knowledge%2DGrounded+Reasoning+with+Large+Language+Models%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2023%2C+pag)
53. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in neural information processing systems, 35:24824–24837.
54. Dustin Wright, David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Isabelle Augenstein, and Lucy Lu Wang. 2022. Generating scientific claims for zero-shot scientific fact checking. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages… [find](https://scholar.google.com/scholar?q=Dustin+Wright%2C+David+Wadden%2C+Kyle+Lo%2C+Bailey+Kuehl%2C+Arman+Cohan%2C+Isabelle+Augenstein%2C+and+Lucy+Lu+Wang%2E+2022%2E+Generating+scientific+claims+for+zero%2Dshot+scientific+fact+checking%2E+In+Proceedings+of+the)
55. Yongjun Xu, Xin Liu, Xin Cao, Changping Huang, Enke Liu, Sen Qian, Xingchen Liu, Yanjun Wu, Fengliang Dong, Cheng-Wei Qiu, and 1 others. 2021. Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4). [find](https://scholar.google.com/scholar?q=Yongjun+Xu%2C+Xin+Liu%2C+Xin+Cao%2C+Changping+Huang%2C+Enke+Liu%2C+Sen+Qian%2C+Xingchen+Liu%2C+Yanjun+Wu%2C+Fengliang+Dong%2C+Cheng%2DWei+Qiu%2C+and+1+others%2E+2021%2E+Artificial+intelligence%3A+A+powerful+paradigm+for+scientif)
56. Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob Foerster, Jeff Clune, and David Ha. 2025. [The ai scientist-v2: Workshop-level automated scientific discovery via agentic tree search](https://md.research.elamur.ai/free/en/papers/yamada-2025-ai-scientist-v2-workshop/). arXiv preprint arXiv:2504.08066.
57. Fengzhu Zeng and Wei Gao. 2024. [Justilm: Few-shot justification generation for explainable fact-checking of real-world claims](https://md.research.elamur.ai/free/en/papers/zeng-2024-justilm-few-shot-justification-generation/). Transactions of the Association for Computational Linguistics, 12:334–354.
58. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675. [find](https://scholar.google.com/scholar?q=Tianyi+Zhang%2C+Varsha+Kishore%2C+Felix+Wu%2C+Kilian+Q+Weinberger%2C+and+Yoav+Artzi%2E+2019%2E+Bertscore%3A+Evaluating+text+generation+with+bert%2E+arXiv+preprint+arXiv%3A1904%2E09675%2E)

*Updated 1 November 2026*