arXiv:2604.06484v3 Announce Type: replace Abstract: Cultural values are expressed not only through language but also through visual scenes and everyday social practices. Yet existing evaluations of cultural values in language models are almost entirely text-only, leaving it unclear whether culture-conditioned judgments remain stable when response options are visualized. We introduce ValueGround, a benchmark for evaluating culture-conditioned visual value grounding in multimodal large language models (MLLMs). Built from World Values Survey questions, ValueGround uses minimally contrastive image
The rapid advancement and widespread deployment of Multimodal Large Language Models (MLLMs) necessitate robust evaluation benchmarks, especially concerning nuanced cultural understanding.
This benchmark addresses a critical gap in assessing MLLMs' ability to ground cultural values visually, which is crucial for their ethical deployment and global applicability.
The introduction of ValueGround shifts MLLM evaluation beyond text-only cultural assessments, moving towards more comprehensive, visually-integrated understanding, which will influence future model development priorities.
- · AI ethics researchers
- · MLLM developers
- · Social scientists
- · Users in diverse cultural contexts
- · MLLMs with poor visual cultural grounding
- · Developers neglecting cultural nuance
Researchers will start fine-tuning and evaluating MLLMs specifically against the ValueGround benchmark, leading to improved culturally-aware models.
Enhanced cultural understanding in MLLMs will reduce biases and improve their utility in highly diverse global markets, potentially accelerating adoption in new regions.
The ability of AI to accurately perceive and act upon deeply embedded cultural visual cues could lead to more sophisticated AI agents capable of navigating complex human social dynamics.
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Read at arXiv cs.CL