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arXiv · 2601.04693

Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding

Sungmok Jung·Yeonkyoung So·Joonhak Lee·Sangho Kim·Yelim Ahn·Jaejin Lee
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Abstract

Although negation is known to challenge large language models (LLMs), benchmarks for evaluating negation understanding-especially in Korean-are scarce. We conduct a corpus-based analysis of Korean negation and show that LLM performance degrades under negation. We then introduce Thunder-KoNUBench, a sentence-level negation understanding benchmark that reflects the empirical distribution of Korean negation phenomena. Evaluating 47 LLMs on Thunder-KoNUBench, we analyze the effects of model size and instruction tuning, and perform error analysis to better understand model behavior. We further show that fine-tuning on Thunder-KoNUBench improves negation understanding and broader contextual comprehension in Korean.

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Sungmok Jung, Yeonkyoung So, Joonhak Lee, Sangho Kim, Yelim Ahn, Jaejin Lee. 2026-01-08. Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding. https://arxiv.org/abs/2601.04693

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