大型视觉语言模型中的跨文化价值观归因研究

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Abstract:The rapid adoption of large vision-language models (LVLMs) in recent years has been accompanied by growing fairness concerns due to their propensity to reinforce harmful societal stereotypes. While significant attention has been paid to such fairness concerns in the context of social biases, relatively little prior work has examined the presence of stereotypes in LVLMs related to cultural contexts such as religion, nationality, and socioeconomic status. In this work, we aim to narrow this gap by investigating how LVLM judgments about a person's moral, ethical, and political values vary across cultural contexts presented in images. We conduct a multi-dimensional analysis of such value judgments in popular LVLMs using counterfactual image sets, which depict the same person across different cultural contexts. Our evaluation framework pairs descriptive analyses (Moral Foundations Theory categorization, lexical analyses, and value sensitivity) with a novel grounding analysis that compares LVLM cross-context variation against two large-scale human surveys (MFQ-2 and WVS Wave 7). Across 4.8 million LVLM generations, we identify three survey-grounding bias patterns that replicate across multiple architecturally diverse models. Additional ablations show that nationality grounding is text-dominant while religion and socioeconomic grounding depend strongly on the image, and that image conditioning can amplify survey-grounding bias patterns.

Comments:	Accepted to EMNLP 2026 Findings
Subjects:	Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as:	arXiv:2604.09945 [cs.CV]
	(or arXiv:2604.09945v3 [cs.CV] for this version)
	https://doi.org/10.48550/arXiv.2604.09945 arXiv-issued DOI via DataCite

Submission history

From: Phillip Howard [view email]
[v1] Fri, 10 Apr 2026 22:53:41 UTC (4,324 KB)
[v2] Thu, 2 Jul 2026 01:29:14 UTC (21,589 KB)
[v3] Mon, 5 Oct 2026 22:09:32 UTC (21,600 KB)