We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. Unlike previous Korean benchmarks that are translated from existing English benchmarks, KMMLU is collected from original Korean exams, capturing linguistic and cultural aspects of the Korean language. We test 26 publically available and proprietary LLMs, identifying significant room for improvement. The best publicly available model achieves 50.54% on KMMLU, far below the average human performance of 62.6%. This model was primarily trained for English and Chinese, not Korean. Current LLMs tailored to Korean, such as Polyglot-Ko, perform far worse. Surprisingly, even the most capable proprietary LLMs, e.g., GPT-4 and HyperCLOVA X, achieve 59.95% and 53.40%, respectively. This suggests that further work is needed to improve Korean LLMs, and KMMLU offers the right tool to track this progress. We make our dataset publicly available on the Hugging Face Hub and integrate the benchmark into EleutherAI's Language Model Evaluation Harness.

我们提出了KMMLU，这是一个新的韩语基准，包括来自45个学科的35,030个专家级多项选择题，涵盖人文学科到STEM学科。与之前从现有英语基准翻译而来的韩语基准不同，KMMLU收集了来自原始韩语考试的问题，捕捉了韩语的语言和文化方面。我们测试了26个公开和专有LLM模型，发现有显著的改进空间。最好的公开模型在KMMLU上的准确率为50.54％，远远低于人类平均表现62.6％。该模型主要用于英文和中文训练，而不是韩语。对于韩语，当前的适用LLMs，例如Polyglot-Ko，表现得更差。令人惊讶的是，即使是最强大的专有LLMs，例如GPT-4和HyperCLOVA X，分别只能达到59.95％和53.40％。这表明需要进一步改进韩语LLMs，而KMMLU提供了追踪这一进展的正确工具。我们在Hugging Face Hub上公开了我们的数据集，并将这个基准整合到EleutherAI的语言模型评估工具中。

KMMLU：韩国语的大规模多任务语言理解测量