Despite the existence of various benchmarks for evaluating natural language processing models, we argue that human exams are a more suitable means of evaluating general intelligence for large language models (LLMs), as they inherently demand a much wider range of abilities such as language understanding, domain knowledge, and problem-solving skills. To this end, we introduce M3Exam, a novel benchmark sourced from real and official human exam questions for evaluating LLMs in a multilingual, multimodal, and multilevel context. M3Exam exhibits three unique characteristics: (1) multilingualism, encompassing questions from multiple countries that require strong multilingual proficiency and cultural knowledge; (2) multimodality, accounting for the multimodal nature of many exam questions to test the model's multimodal understanding capability; and (3) multilevel structure, featuring exams from three critical educational periods to comprehensively assess a model's proficiency at different levels. In total, M3Exam contains 12,317 questions in 9 diverse languages with three educational levels, where about 23\% of the questions require processing images for successful solving. We assess the performance of top-performing LLMs on M3Exam and find that current models, including GPT-4, still struggle with multilingual text, particularly in low-resource and non-Latin script languages. Multimodal LLMs also perform poorly with complex multimodal questions. We believe that M3Exam can be a valuable resource for comprehensively evaluating LLMs by examining their multilingual and multimodal abilities and tracking their development. Data and evaluation code is available at \url{https://github.com/DAMO-NLP-SG/M3Exam}.

翻译：尽管已有多种用于评估自然语言处理模型的基准，但我们认为人类考试是评估大语言模型通用智能的更合适手段，因为这类考试天然要求更广泛的能力，如语言理解、领域知识和问题解决技能。为此，我们提出M3Exam，一个源自真实官方人类考试题目的新型基准，用于在多语言、多模态、多层次背景下评估大语言模型。M3Exam具有三个独特特性：(1)多语言性，包含来自多个国家的试题，需具备强大的多语言能力和文化知识；(2)多模态性，考虑许多考试题目的多模态特性，以测试模型的多模态理解能力；(3)多层次结构，涵盖三个关键教育阶段的考试，以全面评估模型在不同水平上的能力。M3Exam总计包含来自9种多样语言、三个教育层次的12,317道题目，其中约23%的题目需处理图像才能成功解答。我们评估了顶尖大语言模型在M3Exam上的表现，发现当前模型（包括GPT-4）在多语言文本处理上仍存在困难，尤其是在低资源和非拉丁文字语言中。多模态大语言模型在复杂多模态问题上表现也不佳。我们相信M3Exam可通过考察大语言模型的多语言和多模态能力并追踪其发展，成为全面评估的宝贵资源。数据和评估代码见\url{https://github.com/DAMO-NLP-SG/M3Exam}。