SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia
Chaoqun Liu, Wenxuan Zhang, Jiahao Ying, Mahani Aljunied, Anh Tuan Luu, Lidong Bing · 2025
This study introduces two novel benchmarks, SeaExam and SeaBench, designed to evaluate the capabilities of Large Language Models (LLMs) in Southeast Asian (SEA) application scenarios.Unlike existing multilingual datasets primarily derived from English translations, these benchmarks are constructed based on realworld scenarios from SEA regions.SeaExam draws from regional educational exams to form a comprehensive dataset that encompasses subjects such as local history and literature.In contrast, SeaBench is crafted around multiturn, open-ended tasks that reflect daily interactions within SEA communities.Our evaluations demonstrate that SeaExam and SeaBench more effectively discern LLM performance on SEA language tasks compared to their translated benchmarks.This highlights the importance of using real-world queries to assess the multilingual capabilities of LLMs. 1