BBQ-V：评估大型多模态模型中视觉刻板印象偏见的基准
BBQ-V: Benchmarking Visual Stereotype Bias in Large Multimodal Models
AI总结：
BBQ-V通过真实多演员图像和多样类别评估LMMs的刻板印象偏见，揭示了主流模型在推理链中的偏见问题。
摘要：
大型多模态模型（LMMs）中的刻板印象偏见会加剧有害的社会偏见，损害人工智能应用的公平性和公正性。随着LMMs影响力日益增加，解决和减轻与刻板印象、有害生成和现实场景中模糊假设相关的固有偏见变得至关重要。然而，现有评估LMMs刻板印象偏见的数据集往往缺乏多样性，依赖合成图像，并且通常只包含单个演员的图像，导致在现实视觉情境下的偏见评估存在空白。为了解决使用真实图像进行偏见评估的空白，我们引入了BBQ-Vision（BBQ-V），这是最全面的评估框架，用于在九个多样类别和50个子类别中评估刻板印象偏见，使用真实和多演员图像。BBQ-V基准包含14,144张图像-问题对，并通过精心编排的、视觉相关的场景严格评估LMMs，挑战它们准确推理视觉刻板印象。它提供了一个稳健的评估框架，具有现实视觉样本、图像变化和开放式问题格式。BBQ-V使模型在不同难度水平上推理能力的精确和细致评估成为可能。通过严格测试19种最先进的开源（通用和推理）和闭源LMMs，我们指出，这些表现优异的模型在多个社会刻板印象上往往存在偏见，并展示了思维模型在推理链中诱导更多偏见。该基准代表了推动AI系统公平性和减少有害偏见的重要一步，为更加公平和具有社会责任感的LMMs奠定了基础。我们的数据集和评估代码已公开可用。
英文摘要：
Stereotype biases in Large Multimodal Models (LMMs) perpetuate harmful societal prejudices, undermining the fairness and equity of AI applications. As LMMs grow increasingly influential, addressing and mitigating inherent biases related to stereotypes, harmful generations, and ambiguous assumptions in real-world scenarios has become essential. However, existing datasets evaluating stereotype biases in LMMs often lack diversity, rely on synthetic images, and often have single-actor images, leaving a gap in bias evaluation for real-world visual contexts. To address the gap in bias evaluation using real images, we introduce the BBQ-Vision (BBQ-V), the most comprehensive framework for assessing stereotype biases across nine diverse categories and 50 sub-categories with real and multi-actor images. BBQ-V benchmark contains 14,144 image-question pairs and rigorously evaluates LMMs through carefully curated, visually grounded scenarios, challenging them to reason accurately about visual stereotypes. It offers a robust evaluation framework featuring real-world visual samples, image variations, and open-ended question formats. BBQ-V enables a precise and nuanced assessment of a model's reasoning capabilities across varying levels of difficulty. Through rigorous testing of 19 state-of-the-art open-source (general-purpose and reasoning) and closed-source LMMs, we highlight that these top-performing models are often biased on several social stereotypes, and demonstrate that the thinking models induce more bias in the reasoning chains. This benchmark represents a significant step toward fostering fairness in AI systems and reducing harmful biases, laying the groundwork for more equitable and socially responsible LMMs. Our dataset and evaluation code are publicly available.
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。
