@inproceedings{wang-etal-2025-sciver,
title = "{S}ci{V}er: Evaluating Foundation Models for Multimodal Scientific Claim Verification",
author = "Wang, Chengye and
Shen, Yifei and
Kuang, Zexi and
Cohan, Arman and
Zhao, Yilun",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.420/",
doi = "10.18653/v1/2025.acl-long.420",
pages = "8562--8579",
ISBN = "979-8-89176-251-0",
abstract = "We introduce SciVer, the first benchmark specifically designed to evaluate the ability of foundation models to verify claims within a multimodal scientific context.SciVer consists of 3,000 expert-annotated examples over 1,113 scientific papers, covering four subsets, each representing a common reasoning type in multimodal scientific claim verification. To enable fine-grained evaluation, each example includes expert-annotated supporting evidence.We assess the performance of 21 state-of-the-art multimodal foundation models, including o4-mini, Gemini-2.5-Flash, Llama-3.2-Vision, and Qwen2.5-VL. Our experiment reveals a substantial performance gap between these models and human experts on SciVer.Through an in-depth analysis of retrieval-augmented generation (RAG), and human-conducted error evaluations, we identify critical limitations in current open-source models, offering key insights to advance models' comprehension and reasoning in multimodal scientific literature tasks."
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%0 Conference Proceedings
%T SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification
%A Wang, Chengye
%A Shen, Yifei
%A Kuang, Zexi
%A Cohan, Arman
%A Zhao, Yilun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wang-etal-2025-sciver
%X We introduce SciVer, the first benchmark specifically designed to evaluate the ability of foundation models to verify claims within a multimodal scientific context.SciVer consists of 3,000 expert-annotated examples over 1,113 scientific papers, covering four subsets, each representing a common reasoning type in multimodal scientific claim verification. To enable fine-grained evaluation, each example includes expert-annotated supporting evidence.We assess the performance of 21 state-of-the-art multimodal foundation models, including o4-mini, Gemini-2.5-Flash, Llama-3.2-Vision, and Qwen2.5-VL. Our experiment reveals a substantial performance gap between these models and human experts on SciVer.Through an in-depth analysis of retrieval-augmented generation (RAG), and human-conducted error evaluations, we identify critical limitations in current open-source models, offering key insights to advance models’ comprehension and reasoning in multimodal scientific literature tasks.
%R 10.18653/v1/2025.acl-long.420
%U https://aclanthology.org/2025.acl-long.420/
%U https://doi.org/10.18653/v1/2025.acl-long.420
%P 8562-8579
Markdown (Informal)
[SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification](https://aclanthology.org/2025.acl-long.420/) (Wang et al., ACL 2025)
ACL