M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models

• URL: http://arxiv.org/abs/2306.05179v2
• Date: Fri, 10 Nov 2023 04:11:01 GMT
• Title: M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models
• Authors: Wenxuan Zhang, Sharifah Mahani Aljunied, Chang Gao, Yew Ken Chia, Lidong Bing
• Abstract summary: M3Exam is a benchmark for evaluating large language models (LLMs) in a multilingual, multimodal, and multilevel context. M3Exam contains 12,317 questions in 9 diverse languages with three educational levels. We assess the performance of top-performing LLMs on M3Exam and find that current models, including GPT-4, still struggle with multilingual text.
• Score: 76.88692952308084
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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}.

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