@inproceedings{jung-etal-2026-thunder,
title = "Thunder-{K}o{NUB}ench: A Corpus-Aligned Benchmark for {K}orean Negation Understanding",
author = "Jung, Sungmok and
So, Yeonkyoung and
Lee, Joonhak and
Kim, Sangho and
Ahn, Yelim and
Lee, Jaejin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.324/",
pages = "6491--6518",
ISBN = "979-8-89176-395-1",
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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<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.</abstract>
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%0 Conference Proceedings
%T Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding
%A Jung, Sungmok
%A So, Yeonkyoung
%A Lee, Joonhak
%A Kim, Sangho
%A Ahn, Yelim
%A Lee, Jaejin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F jung-etal-2026-thunder
%X 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.
%U https://aclanthology.org/2026.findings-acl.324/
%P 6491-6518
Markdown (Informal)
[Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understanding](https://aclanthology.org/2026.findings-acl.324/) (Jung et al., Findings 2026)
ACL