Year: 2026
Type: conference-paper
Abstract: 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.
Authors: Yeonkyoung So, Gyuseong Lee, Sungmok Jung, Joonhak Lee, JiA Kang, Sangho Kim, Jaejin Lee
Institution: Seoul National University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funders: National Research Foundation, Seoul National University, Korea Health Industry Development Institute, National Research Foundation of Korea, Ministry of Education, India, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion, Department of Computer Science and Engineering, Seoul National University
OpenAlex ID: W4415109432