  The evaluation of large language models (LLMs) has drawn substantial
attention in the field recently. This work focuses on evaluating LLMs in a
Chinese context, specifically, for Traditional Chinese which has been largely
underrepresented in existing benchmarks. We present TMLU, a holistic evaluation
suit tailored for assessing the advanced knowledge and reasoning capability in
LLMs, under the context of Taiwanese Mandarin. TMLU consists of an array of 37
subjects across social science, STEM, humanities, Taiwan-specific content, and
others, ranging from middle school to professional levels. In addition, we
curate chain-of-thought-like few-shot explanations for each subject to
facilitate the evaluation of complex reasoning skills. To establish a
comprehensive baseline, we conduct extensive experiments and analysis on 24
advanced LLMs. The results suggest that Chinese open-weight models demonstrate
inferior performance comparing to multilingual proprietary ones, and
open-weight models tailored for Taiwanese Mandarin lag behind the
Simplified-Chinese counterparts. The findings indicate great headrooms for
improvement, and emphasize the goal of TMLU to foster the development of
localized Taiwanese-Mandarin LLMs. We release the benchmark and evaluation
scripts for the community to promote future research.
