超越文本：探索多模态模型中的文化价值敏感性
Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Models
AI总结：
该研究聚焦多模态模型的文化价值对齐问题，评估不同规模视觉语言模型的文化价值敏感性，发现其对齐效果高度依赖语境，揭示了多模态模型价值对齐的复杂性与待解挑战。
摘要：
基于文化背景研究大语言模型（Large Language Models, LLMs）的价值对齐已成为关键研究领域。然而，大型视觉语言模型（vision-language models, VLMs）中的类似偏见尚未得到广泛探究。随着多模态模型规模持续增长，评估图像能否作为文化的可靠代理，以及这些价值如何通过视觉与文本数据的融合嵌入，变得愈发重要。本文对不同规模的多模态模型开展了全面评估，重点关注其与文化价值的对齐情况。研究结果表明，与LLMs类似，VLMs也表现出对文化价值的敏感性，但它们在价值对齐上的表现高度依赖语境。尽管VLMs在通过图像提升价值理解方面展现出潜力，但这种对齐效果在不同语境下差异显著，凸显了多模态模型对齐领域的复杂性与尚未充分探索的挑战。
英文摘要：
Investigating value alignment in Large Language Models (LLMs) based on cultural context has become a critical area of research. However, similar biases have not been extensively explored in large vision-language models (VLMs). As the scale of multimodal models continues to grow, it becomes increasingly important to assess whether images can serve as reliable proxies for culture and how these values are embedded through the integration of both visual and textual data. In this paper, we conduct a thorough evaluation of multimodal model at different scales, focusing on their alignment with cultural values. Our findings reveal that, much like LLMs, VLMs exhibit sensitivity to cultural values, but their performance in aligning with these values is highly context-dependent. While VLMs show potential in improving value understanding through the use of images, this alignment varies significantly across contexts highlighting the complexities and underexplored challenges in the alignment of multimodal models.
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