From KMMLU-Redux to KMMLU-Pro: A Professional Korean Benchmark Suite for
  LLM Evaluation
Authors: name: Seokhee Hong, name: Sunkyoung Kim, name: Guijin Son, name: Soyeon Kim, name: Yeonjung Hong, name: Jinsik Lee

The development of Large Language Models (LLMs) requires robust benchmarks
that encompass not only academic domains but also industrial fields to
effectively evaluate their applicability in real-world scenarios. In this
paper, we introduce two Korean expert-level benchmarks. KMMLU-Redux,
reconstructed from the existing KMMLU, consists of questions from the Korean
National Technical Qualification exams, with critical errors removed to enhance
reliability. KMMLU-Pro is based on Korean National Professional Licensure exams
to reflect professional knowledge in Korea. Our experiments demonstrate that
these benchmarks comprehensively represent industrial knowledge in Korea. We
release our dataset publicly available.