Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understanding

• URL: http://arxiv.org/abs/2506.14397v2
• Date: Wed, 18 Jun 2025 01:18:11 GMT
• Title: Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understanding
• Authors: Yeonkyoung So, Gyuseong Lee, Sungmok Jung, Joonhak Lee, JiA Kang, Sangho Kim, Jaejin Lee,
• Abstract summary: Negation is a fundamental linguistic phenomenon that poses persistent challenges for Large Language Models.<n>Existing benchmarks often treat negation as a side case within broader tasks like natural language inference.<n>We introduce Thunder-NUBench, a novel benchmark explicitly designed to assess sentence-level negation understanding in LLMs.
• Score: 4.9301587184653295
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Negation is a fundamental linguistic phenomenon that poses persistent challenges for Large Language Models (LLMs), particularly in tasks requiring deep semantic understanding. Existing benchmarks often treat negation as a side case within broader tasks like natural language inference, resulting in a lack of benchmarks that exclusively target negation understanding. In this work, we introduce Thunder-NUBench, a novel benchmark explicitly designed to assess sentence-level negation understanding in LLMs. Thunder-NUBench goes beyond surface-level cue detection by contrasting standard negation with structurally diverse alternatives such as local negation, contradiction, and paraphrase. The benchmark consists of manually curated sentence-negation pairs and a multiple-choice dataset that enables in-depth evaluation of models' negation understanding.

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