HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
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Computer Science > Computation and Language
arXiv:2506.03922 (cs)
[Submitted on 4 Jun 2025 (v1), last revised 11 Aug 2026 (this version, v4)]
Title:HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
Authors:Zhaolu Kang, Junhao Gong, Jiaxu Yan, Wanke Xia, Yian Wang, Ziwen Wang, Huaxuan Ding, Zhuo Cheng, Wenhao Cao, Zhiyuan Feng, Siqi He, Shannan Yan, Junzhe Chen, Xiaomin He, Chaoya Jiang, Wei Ye, Kaidong Yu, Xuelong Li
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Abstract:Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of domains. However, current benchmarks for evaluating MLLMs primarily emphasize general knowledge and vertical step-by-step reasoning typical of STEM disciplines, while overlooking the distinct needs and potential of the Humanities and Social Sciences (HSS). Tasks in the HSS domain require more horizontal, interdisciplinary thinking and a deep integration of knowledge across related fields, which presents unique challenges for MLLMs, particularly in linking abstract concepts with corresponding visual representations. Addressing this gap, we present HSSBench, a dedicated benchmark designed to assess the capabilities of MLLMs on HSS tasks in multiple languages, including the six official languages of the United Nations. We also introduce a novel data generation pipeline tailored for HSS scenarios, in which multiple domain experts and automated agents collaborate to generate and iteratively refine each sample. HSSBench contains over 13,000 meticulously designed samples, covering six key categories. We benchmark more than 20 mainstream MLLMs on HSSBench and demonstrate that it poses significant challenges even for state-of-the-art models. We hope that this benchmark will inspire further research into enhancing the cross-disciplinary reasoning abilities of MLLMs, especially their capacity to internalize and connect knowledge across fields.
| Comments: | ICLR 2026 (OpenReview: this https URL) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2506.03922 [cs.CL] |
| (or arXiv:2506.03922v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2506.03922
arXiv-issued DOI via DataCite
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Submission history
From: Zhaolu Kang [view email][v1] Wed, 4 Jun 2025 13:14:13 UTC (38,873 KB)
[v2] Tue, 24 Feb 2026 09:07:33 UTC (36,086 KB)
[v3] Tue, 3 Mar 2026 14:00:03 UTC (36,086 KB)
[v4] Tue, 11 Aug 2026 06:44:38 UTC (36,086 KB)
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