@inproceedings{so-etal-2026-thunder,
    title = "Thunder-{NUB}ench: A Benchmark for {LLM}s' Sentence-Level Negation Understanding",
    author = "So, Yeonkyoung  and
      Lee, Gyuseong  and
      Jung, Sungmok  and
      Lee, Joonhak  and
      Kang, JiA  and
      Kim, Sangho  and
      Lee, Jaejin",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.250/",
    pages = "4749--4793",
    ISBN = "979-8-89176-386-9",
    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."
}