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        <title>SciTrue: Evidence-Grounded Claim Verification in Science</title>
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            <namePart type="given">Danilo</namePart>
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    <abstract>Large language models (LLMs) have expanded the potential for AI-assisted scientific claim verification, yet existing systems often exhibit unverifiable attributions, shallow evidence mapping, and hallucinated citations. We present SciTrue, a claim verification system providing source-level accountability and evidence traceability. SciTrue links each claim component to explicit, verifiable scientific sources, enabling users to inspect and challenge model inferences, addressing limitations of both general-purpose and search-augmented LLMs. In a human evaluation of 300 attributions, SciTrue achieves high fidelity in summary traceability, attribution accuracy, and context alignment, substantially outperforming RAG-based baselines such as GPT-4o-search-preview and Perplexity Sonar Pro. These results underscore the importance of principled attribution and context-aware reasoning in AI-assisted scientific verification. A system demo is available at .</abstract>
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%0 Conference Proceedings
%T SciTrue: Evidence-Grounded Claim Verification in Science
%A Tan, Neset
%A Li, Minghao
%A Gahegan, Mark
%Y Croce, Danilo
%Y Leidner, Jochen
%Y Moosavi, Nafise Sadat
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Marocco
%@ 979-8-89176-382-1
%F tan-etal-2026-scitrue
%X Large language models (LLMs) have expanded the potential for AI-assisted scientific claim verification, yet existing systems often exhibit unverifiable attributions, shallow evidence mapping, and hallucinated citations. We present SciTrue, a claim verification system providing source-level accountability and evidence traceability. SciTrue links each claim component to explicit, verifiable scientific sources, enabling users to inspect and challenge model inferences, addressing limitations of both general-purpose and search-augmented LLMs. In a human evaluation of 300 attributions, SciTrue achieves high fidelity in summary traceability, attribution accuracy, and context alignment, substantially outperforming RAG-based baselines such as GPT-4o-search-preview and Perplexity Sonar Pro. These results underscore the importance of principled attribution and context-aware reasoning in AI-assisted scientific verification. A system demo is available at .
%U https://aclanthology.org/2026.eacl-demo.27/
%P 397-406

Markdown (Informal)

[SciTrue: Evidence-Grounded Claim Verification in Science](https://aclanthology.org/2026.eacl-demo.27/) (Tan et al., EACL 2026)

• SciTrue: Evidence-Grounded Claim Verification in Science (Tan et al., EACL 2026)

ACL

• Neset Tan, Minghao Li, and Mark Gahegan. 2026. SciTrue: Evidence-Grounded Claim Verification in Science. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 397–406, Rabat, Marocco. Association for Computational Linguistics.