MM-MoralBench: A MultiModal Moral Evaluation Benchmark for Large Vision-Language Models

[2412.20718] MM-MoralBench: A MultiModal Moral Evaluation Benchmark for Large Vision-Language Models Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2412.20718 (cs) [Submitted on 30 Dec 2024 ( v1 ), last revised 8 Apr 2026 (this version, v2)] Title: MM-MoralBench: A MultiModal Moral Evaluation Benchmark for Large Vision-Language Models Authors: Bei Yan , Jie Zhang , Zhiyuan Chen , Shiguang Shan , Xilin Chen View a PDF of the paper titled MM-MoralBench: A MultiModal Moral Evaluation Benchmark for Large Vision-Language Models, by Bei Yan and 4 other authors View PDF HTML (experimental) Abstract: The rapid integration of Large Vision-Language Models (LVLMs) into critical domains necessitates comprehensive moral evaluation to ensure their alignment with human values. While extensive research has addressed moral evaluation in LLMs, text-centric assessments cannot adequately capture the complex contextual nuances and ambiguities introduced by visual modalities. To bridge this gap, we introduce MM-MoralBench, a multimodal moral evaluation benchmark grounded in Moral Foundations Theory. We construct unique multimodal scenarios by combining synthesized visual contexts with character dialogues to simulate real-world dilemmas where visual and linguistic information interact dynamically. Our benchmark assesses models across six moral foundations through moral judgment, classification, and response tasks. Extensive evaluations of over 20 LVLMs reveal that models exhibit pronounced moral alignment bias, diverging significantly from human consensus. Furthermore, our analysis indicates that general scaling or structural improvements yield diminishing returns in moral alignment, and thinking paradigm may trigger overthinking-induced failures in moral contexts, highlighting the necessity for targeted moral alignment strategies. Our benchmark is publicly available. Comments: Accepted by Pattern Recognition Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2412.20718 [cs.CV] (or arXiv:2412.20718v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2412.20718 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Bei Yan [ view email ] [v1] Mon, 30 Dec 2024 05:18:55 UTC (2,546 KB) [v2] Wed, 8 Apr 2026 17:39:16 UTC (3,688 KB) Full-text links: Access Paper: View a PDF of the paper titled MM-MoralBench: A MultiModal Moral Evaluation Benchmark for Large Vision-Language Models, by Bei Yan and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-12 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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