多种方言，多种语言，一种文化视角：评估多语言视觉语言模型对孟加拉文化的理解，涵盖历史关联语言和地区方言
Many Dialects, Many Languages, One Cultural Lens: Evaluating Multilingual VLMs for Bengali Culture Understanding Across Historically Linked Languages and Regional Dialects
AI总结：
提出 BanglaVerse 基准，通过手工标注图像和扩展至多种语言及方言，评估多语言视觉语言模型在孟加拉文化理解中的表现，发现标准孟加拉语评估高估模型能力，方言变化导致性能下降，文化知识缺失是主要瓶颈。
摘要：
孟加拉文化通过地区、方言、历史、食物、政治、媒体和日常视觉生活丰富地表达，但在多模态评估中仍然代表性不足。为了解决这一差距，我们引入了BanglaVerse，这是一个文化基础的基准，用于评估多语言视觉语言模型（VLM）对孟加拉文化的理解，涵盖历史关联语言和地区方言。该基准由9个领域的1152张手动策划图像构建，支持视觉问答和字幕生成，并扩展为四种语言和五种孟加拉方言，产生约32.2K个工件。我们的实验表明，仅评估标准孟加拉语会高估真实模型能力：在方言变化下性能下降，尤其是字幕生成，而历史关联语言如印地语和乌尔都语保留了一些文化意义，但在结构化推理方面仍然较弱。跨领域来看，主要瓶颈是缺失文化知识而非仅视觉基础，尤其是知识密集型类别。这些发现将BanglaVerse定位为在语言变化下衡量文化基础多模态理解的更现实测试平台。
英文摘要：
Bangla culture is richly expressed through region, dialect, history, food, politics, media, and everyday visual life, yet it remains underrepresented in multimodal evaluation. To address this gap, we introduce BanglaVerse, a culturally grounded benchmark for evaluating multilingual vision-language models (VLMs) on Bengali culture across historically linked languages and regional dialects. Built from 1,152 manually curated images across nine domains, the benchmark supports visual question answering and captioning, and is expanded into four languages and five Bangla dialects, yielding ~32.2K artifacts. Our experiments show that evaluating only standard Bangla overestimates true model capability: performance drops under dialectal variation, especially for caption generation, while historically linked languages such as Hindi and Urdu retain some cultural meaning but remain weaker for structured reasoning. Across domains, the main bottleneck is missing cultural knowledge rather than visual grounding alone, with knowledge-intensive categories. These findings position BanglaVerse as a more realistic test bed for measuring culturally grounded multimodal understanding under linguistic variation.
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。
