bibtext: @inproceedings{
  tam2024tmmlu,
  title={{TMMLU}+: An Improved Traditional Chinese Evaluation Suite for Foundation Models},
  author={Zhi Rui Tam and Ya Ting Pai and Yen-Wei Lee and Hong-Han Shuai and Jun-Da Chen and Wei Min Chu and Sega Cheng},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=95TayIeqJ4}
  }
abstract: We present TMMLU+, a new benchmark designed for Traditional Chinese language understanding. TMMLU+ is a multi-choice question-answering dataset with 66 subjects from elementary to professional level. It is six times larger and boasts a more balanced subject distribution than its predecessor, Taiwan Massive Multitask Language Understanding (TMMLU). We also benchmark closed-source models and 26 open-weight Chinese large language models (LLMs) of parameters ranging from 1.8B to 72B on the proposed TMMLU+. Our findings reveal that (1.) Traditional Chinese models still trail behind their Simplified Chinese counterparts, highlighting a need for more focused advancements in LLMs catering to Traditional Chinese. (2.) Current LLMs still fall short of human performance in average scores, indicating a potential need for future research to delve deeper into social science and humanities subjects. (3.) Among all the tokenization compression metrics examined, we identify that only the fertility score uniquely demonstrates strong correlations with our benchmark results. We foresee that TMMLU+ will pinpoint areas for future model improvement, thereby narrowing the gap between machine and human linguistic capabilities and supporting researchers in developing Traditional Chinese LLMs. Our dataset, along with the benchmark source code, is accessible at huggingface.co/datasets/ikala/tmmluplus.
title: TMMLU+: An Improved Traditional Chinese Evaluation Suite for Foundation Models
authors: Zhi Rui Tam Ya Ting Pai Yen-Wei Lee Hong-Han Shuai Jun-Da Chen Wei Min Chu Sega Cheng