MM-SCALE: Grounded Multimodal Moral Reasoning via Scalar Judgment and Listwise Alignment

• URL: http://arxiv.org/abs/2602.03665v1
• Date: Tue, 03 Feb 2026 15:48:00 GMT
• Title: MM-SCALE: Grounded Multimodal Moral Reasoning via Scalar Judgment and Listwise Alignment
• Authors: Eunkyu Park, Wesley Hanwen Deng, Cheyon Jin, Matheus Kunzler Maldaner, Jordan Wheeler, Jason I. Hong, Hong Shen, Adam Perer, Ken Holstein, Motahhare Eslami, Gunhee Kim,
• Abstract summary: We present MM-SCALE, a dataset for aligning Vision-Language Models with human moral preferences.<n>Each image-scenario pair is annotated with moral acceptability scores and grounded reasoning labels by humans.<n>Our framework provides richer alignment signals and finer calibration of multimodal moral reasoning.
• Score: 48.39756797294967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) continue to struggle to make morally salient judgments in multimodal and socially ambiguous contexts. Prior works typically rely on binary or pairwise supervision, which often fail to capture the continuous and pluralistic nature of human moral reasoning. We present MM-SCALE (Multimodal Moral Scale), a large-scale dataset for aligning VLMs with human moral preferences through 5-point scalar ratings and explicit modality grounding. Each image-scenario pair is annotated with moral acceptability scores and grounded reasoning labels by humans using an interface we tailored for data collection, enabling listwise preference optimization over ranked scenario sets. By moving from discrete to scalar supervision, our framework provides richer alignment signals and finer calibration of multimodal moral reasoning. Experiments show that VLMs fine-tuned on MM-SCALE achieve higher ranking fidelity and more stable safety calibration than those trained with binary signals.

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