识别符号，缺失文化：探究视觉-语言模型在火 imagery 和文化意义上的推理
Seeing Symbols, Missing Cultures: Probing Vision-Language Models' Reasoning on Fire Imagery and Cultural Meaning
AI总结：
研究发现视觉-语言模型在处理火主题文化图像时存在系统性偏见，需通过文化评估确保公平性和可解释性。
摘要：
视觉-语言模型（VLMs）往往表现出文化能力，但依赖于表面模式匹配而非真正的文化理解。我们引入了一个诊断框架，通过分类和解释分析来探测VLMs在火主题文化图像上的推理。测试多个模型在西方节日、非西方传统和紧急场景上揭示了系统性偏见：模型能够正确识别显眼的西方节日，但在代表性较低的文化事件上挣扎，经常提供模糊的标签或危险地将紧急情况误分类为庆祝活动。这些失败暴露了符号捷径的风险，并突显了在准确性指标之外进行文化评估的必要性，以确保可解释和公平的多模态系统。
英文摘要：
Vision-Language Models (VLMs) often appear culturally competent but rely on superficial pattern matching rather than genuine cultural understanding. We introduce a diagnostic framework to probe VLM reasoning on fire-themed cultural imagery through both classification and explanation analysis. Testing multiple models on Western festivals, non-Western traditions, and emergency scenes reveals systematic biases: models correctly identify prominent Western festivals but struggle with underrepresented cultural events, frequently offering vague labels or dangerously misclassifying emergencies as celebrations. These failures expose the risks of symbolic shortcuts and highlight the need for cultural evaluation beyond accuracy metrics to ensure interpretable and fair multimodal systems.
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。
