@inproceedings{hong-etal-2025-kmmlu,
title = "From {KMMLU}-Redux to Pro: A Professional {K}orean Benchmark Suite for {LLM} Evaluation",
author = "Hong, Seokhee and
Kim, Sunkyoung and
Son, Guijin and
Kim, Soyeon and
Hong, Yeonjung and
Lee, Jinsik",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1038/",
pages = "19067--19096",
ISBN = "979-8-89176-335-7",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T From KMMLU-Redux to Pro: A Professional Korean Benchmark Suite for LLM Evaluation
%A Hong, Seokhee
%A Kim, Sunkyoung
%A Son, Guijin
%A Kim, Soyeon
%A Hong, Yeonjung
%A Lee, Jinsik
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F hong-etal-2025-kmmlu
%X 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.
%U https://aclanthology.org/2025.findings-emnlp.1038/
%P 19067-19096
Markdown (Informal)
[From KMMLU-Redux to Pro: A Professional Korean Benchmark Suite for LLM Evaluation](https://aclanthology.org/2025.findings-emnlp.1038/) (Hong et al., Findings 2025)
ACL