文化反事实：用反事实示例评估大型视觉语言模型中的文化偏见
Cultural Counterfactuals: Evaluating Cultural Biases in Large Vision-Language Models with Counterfactual Examples
AI总结：
提出文化反事实数据集（近6万张反事实图像），通过图像编辑模型将不同人口特征的人置于真实文化背景中，量化大型视觉语言模型在宗教、国籍和社会经济地位方面的文化偏见。
摘要：
近年来，大型视觉语言模型（LVLMs）变得越来越强大，但也可能表现出有害的偏见。先前研究此类偏见的工作主要关注与图像中人物视觉特征相关的人口统计特征（如种族或性别）。这导致与文化差异（例如宗教、社会经济地位）相关的偏见相对未被充分研究，而这些差异无法仅从个体的外表轻易辨别。衡量文化偏见的一个关键挑战是，确定个体属于哪个群体通常取决于图像中的文化背景线索，而带有文化背景注释的数据集十分匮乏。为解决这一问题，我们引入了文化反事实数据集：一个高质量合成数据集，包含近6万张反事实图像，用于衡量与宗教、国籍和社会经济地位相关的文化偏见。为确保文化背景的准确描绘，我们使用图像编辑模型生成数据集，将不同人口特征的人置于真实的文化背景图像中。这使我们能够构建反事实图像集，展示同一个人在不同背景下的情况，从而精确衡量文化背景差异对LVLM输出的影响。我们展示了文化反事实数据集在量化流行LVLMs中文化偏见的实用性。
英文摘要：
Large Vision-Language Models (LVLMs) have grown increasingly powerful in recent years, but can also exhibit harmful biases. Prior studies investigating such biases have primarily focused on demographic traits related to the visual characteristics of a person depicted in an image, such as their race or gender. This has left biases related to cultural differences (e.g., religion, socioeconomic status), which cannot be readily discerned from an individual's appearance alone, relatively understudied. A key challenge in measuring cultural biases is that determining which group an individual belongs to often depends upon cultural context cues in images, and datasets annotated with cultural context cues are lacking. To address this gap, we introduce Cultural Counterfactuals: a high-quality synthetic dataset containing nearly 60k counterfactual images for measuring cultural biases related to religion, nationality, and socioeconomic status. To ensure that cultural contexts are accurately depicted, we generate our dataset using an image-editing model to place people of different demographics into real cultural context images. This enables the construction of counterfactual image sets which depict the same person in multiple different contexts, allowing for precise measurement of the impact that cultural context differences have on LVLM outputs. We demonstrate the utility of Cultural Counterfactuals for quantifying cultural biases in popular LVLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
