@inproceedings{ho-etal-2026-sciclaimeval,
title = "{S}ci{C}laim{E}val: Cross-modal Claim Verification in Scientific Papers",
author = "Ho, Xanh and
Wu, Yun-Ang and
Kumar, Sunisth and
Xia, Tian Cheng and
Boudin, Florian and
Greiner-Petter, Andre and
Aizawa, Akiko",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.864/",
doi = "10.63317/4ap9rg2gnwmf",
pages = "11060--11071",
abstract = "We present SciClaimEval, a new scientific dataset for the claim verification task. Unlike existing resources, SciClaimEval features authentic claims, including refuted ones, directly extracted from published papers. To create refuted claims, we introduce a novel approach that modifies the supporting evidence (figures and tables), rather than altering the claims or relying on large language models (LLMs) to fabricate contradictions. The dataset provides cross-modal evidence with diverse representations: figures are available as images, while tables are provided in multiple formats, including images, LaTeX source, HTML, and JSON. SciClaimEval contains 1,664 annotated samples from 180 papers across three domains, machine learning, natural language processing, and medicine, validated through expert annotation. We benchmark 11 multimodal foundation models, both open-source and proprietary, across the dataset. Results show that figure-based verification remains particularly challenging for all models, as a substantial performance gap remains between the best system and human baseline."
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<abstract>We present SciClaimEval, a new scientific dataset for the claim verification task. Unlike existing resources, SciClaimEval features authentic claims, including refuted ones, directly extracted from published papers. To create refuted claims, we introduce a novel approach that modifies the supporting evidence (figures and tables), rather than altering the claims or relying on large language models (LLMs) to fabricate contradictions. The dataset provides cross-modal evidence with diverse representations: figures are available as images, while tables are provided in multiple formats, including images, LaTeX source, HTML, and JSON. SciClaimEval contains 1,664 annotated samples from 180 papers across three domains, machine learning, natural language processing, and medicine, validated through expert annotation. We benchmark 11 multimodal foundation models, both open-source and proprietary, across the dataset. Results show that figure-based verification remains particularly challenging for all models, as a substantial performance gap remains between the best system and human baseline.</abstract>
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%0 Conference Proceedings
%T SciClaimEval: Cross-modal Claim Verification in Scientific Papers
%A Ho, Xanh
%A Wu, Yun-Ang
%A Kumar, Sunisth
%A Xia, Tian Cheng
%A Boudin, Florian
%A Greiner-Petter, Andre
%A Aizawa, Akiko
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F ho-etal-2026-sciclaimeval
%X We present SciClaimEval, a new scientific dataset for the claim verification task. Unlike existing resources, SciClaimEval features authentic claims, including refuted ones, directly extracted from published papers. To create refuted claims, we introduce a novel approach that modifies the supporting evidence (figures and tables), rather than altering the claims or relying on large language models (LLMs) to fabricate contradictions. The dataset provides cross-modal evidence with diverse representations: figures are available as images, while tables are provided in multiple formats, including images, LaTeX source, HTML, and JSON. SciClaimEval contains 1,664 annotated samples from 180 papers across three domains, machine learning, natural language processing, and medicine, validated through expert annotation. We benchmark 11 multimodal foundation models, both open-source and proprietary, across the dataset. Results show that figure-based verification remains particularly challenging for all models, as a substantial performance gap remains between the best system and human baseline.
%R 10.63317/4ap9rg2gnwmf
%U https://aclanthology.org/2026.lrec-1.864/
%U https://doi.org/10.63317/4ap9rg2gnwmf
%P 11060-11071
Markdown (Informal)
[SciClaimEval: Cross-modal Claim Verification in Scientific Papers](https://aclanthology.org/2026.lrec-1.864/) (Ho et al., LREC 2026)
ACL
- Xanh Ho, Yun-Ang Wu, Sunisth Kumar, Tian Cheng Xia, Florian Boudin, Andre Greiner-Petter, and Akiko Aizawa. 2026. SciClaimEval: Cross-modal Claim Verification in Scientific Papers. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 11060–11071, Palma de Mallorca, Spain. ELRA Language Resource Association.