LaoBench：一种大规模多维老挝语基准测试用于大语言模型
LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models
AI总结：
本文提出LaoBench，首个大规模多维老挝语基准测试，包含17000+样本，涵盖文化知识应用、K12教育和双语翻译，评估LLM在老挝语的理解与推理能力，发现多语言模型在文化推理和翻译准确性上仍落后于人类专家。
摘要：
大规模语言模型（LLMs）的快速发展未得到低资源语言评估的匹配，尤其是东南亚语言如老挝语。为此，我们引入LaoBench，首个大规模、高质量且多维的基准测试，用于评估LLM在老挝语中的语言理解和推理能力。LaoBench包含17,000+专家精选样本，涵盖文化根基知识应用、课程对齐的K12教育以及老挝、中文和英语之间的双语翻译。它包括开源和保留子集，其中保留部分通过受控服务实现安全的黑盒评估，以提高公平性和数据安全。我们通过专家编写与代理辅助验证相结合的混合流程构建LaoBench，确保语言准确性、文化相关性和教育有效性。我们评估了多种先进的开源和闭源LLMs，发现即使强大的多语言模型在文化根基推理和翻译准确性上仍落后于人类专家。我们希望LaoBench能推动对老挝语和其他未被代表的东南亚语言的研究，以实现更包容的多语言评估。
英文摘要：
The rapid advancement of large language models (LLMs) has not been matched by their evaluation in low-resource languages, especially Southeast Asian languages like Lao. To fill this gap, we introduce \textbf{LaoBench}, the first large-scale, high-quality, and multidimensional benchmark for assessing LLM language understanding and reasoning in Lao. LaoBench contains \textbf{17,000+} expert-curated samples across three dimensions: culturally grounded knowledge application, curriculum-aligned K12 education, and bilingual translation among Lao, Chinese, and English. It includes open-source and held-out subsets, where the held-out portion enables secure black-box evaluation via a controlled service to improve fairness and data security. We construct LaoBench with a hybrid pipeline that combines expert authoring with agent-assisted verification, ensuring linguistic accuracy, cultural relevance, and educational validity. We evaluate diverse state-of-the-art open-source and closed-source LLMs, and find that even strong multilingual models lag behind human experts, particularly in culturally grounded reasoning and translation fidelity. We hope LaoBench will catalyze research on Lao and other underrepresented Southeast Asian languages for more inclusive multilingual evaluation.
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。
