Thunder-NUBench: A Benchmark for LLMs’ Sentence-Level Negation Understanding
Y.T. So, Gyuseong Lee, Sungmok Jung, Joonhak Lee, JiA Kang, Sangho Kim, Jae‐Jin Lee · 2026
Negation is a fundamental linguistic phenomenon that poses ongoing challenges for Large Language Models (LLMs), particularly in tasks requiring deep semantic understanding.Current benchmarks often treat negation as a minor detail within broader tasks, such as natural language inference.Consequently, there is a lack of benchmarks specifically designed to evaluate comprehension of negation.In this work, we introduce Thunder-NUBench -a novel benchmark explicitly created to assess sentence-level understanding of negation in LLMs.Thunder-NUBench goes beyond identifying surface-level cues by contrasting standard negation with structurally diverse alternatives, such as local negation, contradiction, and paraphrase.This benchmark includes manually created sentence-negation pairs and a multiple-choice dataset, allowing for a comprehensive evaluation of models' understanding of negation.