评测大模型对印度地方文化的理解能力，发现其有认知但难落地应用。
Through the Prism of Culture: Evaluating LLMs' Understanding of Indian Subcultures and Traditions
- 通过案例研究评估大模型对印度地方文化认知能力
- 模型能描述文化细节但在具体情境中表现不佳
- 用本地语言提示可提升模型的文化敏感度
大型语言模型虽取得显著进展，但仍存在文化偏见问题，常反映主流叙事而忽视边缘亚文化。本研究评估大模型对印度社会中‘小传统’（如种姓、亲属关系、婚姻与宗教等地方性文化实践）的认知与回应能力。通过系列案例研究，考察模型在主流‘大传统’与地方‘小传统’间的平衡能力。探索不同提示策略，并进一步检验使用区域语言提示是否能增强模型的文化敏感性与回答质量。结果表明，尽管模型能描述文化细节，但在具体情境中难以有效应用。据我们所知，这是首个系统分析大模型与印度亚文化互动的研究，为人工智能系统嵌入文化多样性提供了关键洞见。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown remarkable advancements but also raise concerns about cultural bias, often reflecting dominant narratives at the expense of under-represented subcultures. In this study, we evaluate the capacity of LLMs to recognize and accurately respond to the Little Traditions within Indian society, encompassing localized cultural practices and subcultures such as caste, kinship, marriage, and religion. Through a series of case studies, we assess whether LLMs can balance the interplay between dominant Great Traditions and localized Little Traditions. We explore various prompting strategies and further investigate whether using prompts in regional languages enhances the models cultural sensitivity and response quality. Our findings reveal that while LLMs demonstrate an ability to articulate cultural nuances, they often struggle to apply this understanding in practical, context-specific scenarios. To the best of our knowledge, this is the first study to analyze LLMs engagement with Indian subcultures, offering critical insights into the challenges of embedding cultural diversity in AI systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。