MIT CSAIL and NUS Release APAC-LLM: First APAC-Specific Multilingual Reasoning Benchmark

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and the National University of Singapore (NUS) have jointly released APAC-LLM — the first comprehensive evaluation benchmark for large language model performance on APAC-language reasoning tasks, spanning Japanese, Korean, Mandarin Chinese, Bahasa Indonesia, Vietnamese, Thai, and Tagalog across five distinct reasoning categories: factual question answering, logical inference, mathematical reasoning, code generation with APAC-language comments, and reading comprehension from APAC-origin documents.

The APAC-LLM benchmark addresses a significant gap in the AI evaluation ecosystem: existing LLM evaluation benchmarks (MMLU, BIG-bench, HellaSwag) were designed primarily to evaluate English-language performance, and their Asian-language subsets — where they exist — typically cover only Mandarin Chinese and Japanese at limited coverage depth. APAC AI researchers evaluating whether GPT-4o, Claude 3.5 Sonnet, Gemini 2.0, or Llama 4 is the appropriate foundation model for a Southeast Asian NLP application have had no shared benchmark for rigorous cross-model performance comparison in the specific language and reasoning context relevant to APAC applications.

APAC-LLM's initial benchmark results — published alongside the benchmark framework — reveal significant performance variation across models that English-language benchmarks do not predict: models that score comparably on MMLU show divergent performance on APAC-LLM's Thai and Vietnamese reasoning tasks, suggesting that English benchmark performance is not a reliable predictor of APAC-language reasoning quality. For APAC AI engineers selecting foundation models for production deployment in Southeast Asian markets, APAC-LLM provides the first objective comparative data for the specific languages and reasoning types that APAC production applications require.

The benchmark is released under an open research licence and is available through Hugging Face Datasets, enabling APAC AI research teams to run APAC-LLM evaluations on any LLM — including private or proprietary models deployed on APAC cloud infrastructure — and contribute results to the shared benchmark leaderboard that the MIT-NUS collaboration will maintain.