arXiv 2504.059952026-04-07cs.CLcs.AI

NativQA框架：使LLMs和VLMs具备原生、本地和日常知识

NativQA Framework: Enabling LLMs and VLMs with Native, Local, and Everyday Knowledge

Qatar Computing Research Institute, Qatar（卡塔尔计算研究所）；University of Toronto, Canada（多伦多大学）；UPES, India（印度UPES大学）；Qatar University, Qatar（卡塔尔大学）

Firoj Alam, Md Arid Hasan, Sahinur Rahman Laskar, Mucahid Kutlu, Kareem Darwish, Shammur Absar Chowdhury

备注：: LLMs, Native, Multilingual, Language Diversity, Contextual Understanding, Minority Languages, Culturally Informed, Foundation Models, Large Language Models

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AI总结：

本文提出NativQA框架，通过整合多模态数据，构建本地化问答数据集，提升不同语言和文化背景下的模型性能。

摘要：

大型语言模型（LLMs）的快速发展引发了关于文化偏见、公平性和多语言及欠发达地区表现的担忧。为解决这些差距，需要基于多语言、本地和文化背景的大规模资源。我们系统化并扩展了早期的NativQA框架，通过添加图像、音频和视频支持，使其能够构建在本地语言中文化上和地域上一致的问答数据集。给定用户定义的种子查询，该框架使用搜索引擎收集特定位置的日常信息。我们评估了39个地点、24个国家和7种语言的情况，覆盖了从极低资源到高资源的设置，并收集了超过30万文本问答对、312万图像和29万视频及其相关音频。开发的资源可用于LLMs基准测试和进一步微调。该框架已公开发布供社区使用（https://gitlab.com/nativqa/nativqa-framework）。演示视频可通过此处链接获取：https://shorturl.at/DAVn9。

英文摘要：

The rapid progress of large language models (LLMs) raises concerns about cultural bias, fairness, and performance in diverse languages and underrepresented regions. Addressing these gaps requires large-scale resources grounded in multilingual, local, and cultural contexts. We systematize and extend the earlier NativQA framework to multimodality by adding image, audio, and video support, enabling scalable construction of culturally and regionally aligned QA datasets in native languages. Given user-defined seed queries, the framework uses search engines to collect location-specific everyday information. We evaluate it across 39 locations in 24 countries and 7 languages, spanning extremely low-resource to high-resource settings, and collect over $\sim$300K text QA pairs, $\sim$312K images, and $\sim$29K videos with associated audio. The developed resources can be used for LLMs benchmarking and further fine-tuning. The framework has been made publicly available for the community (https://gitlab.com/nativqa/nativqa-framework). Demo video is available here: \href{https://shorturl.at/DAVn9}{https://shorturl.at/DAVn9}.

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。