🔗 Who cites «Language models are not naysayers: An analysis of language models on negation benchmarks»

46 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · ← back to «Language models are not naysayers: An analysis of language models on negation benchmarks»

1. ArabicMMLU: Assessing Massive Multitask Language Understanding in ArabicFindings of the Association for Computational LinguisticsFajri Koto, Haonan Li, Sara Shatnawi, et al.2024 · 117 citations
2. Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A SurveyarXiv:2404.01869Philipp Mondorf, Barbara Plank2024 · 53 citations
3. Experimental Pragmatics with Machines: Testing LLM Predictions for the Inferences of Plain and Embedded DisjunctionsarXiv:2405.05776Polina Tsvilodub, Paul Marty, Sonia Ramotowska, et al.2024 · 3 citations
4. Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation UnderstandingFindings of the Association for Computational LinguisticsYeonkyoung So, Gyuseong Lee, Sungmok Jung, et al.2026 · 2 citations
5. As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative LanguageTransactions of the Association for Computational Linguistics (TACL)Jasmine Owers, Edwin Simpson, Martha Lewis2026
6. JSTS-Neg: Japanese Semantic Textual Similarity Dataset for Evaluating Negation Understanding AbilityLanguage Resources and Evaluation Conference (LREC)Reiko Yuasa, Yoshihide Kato, Shigeki Matsubara2026
7. NegNLI-BR: A Brazilian Portuguese Benchmark for Negation in Natural Language InferenceLanguage Resources and Evaluation Conference (LREC)Matheus Westhelle, Viviane Moreira2026
8. Spatiotemporal Sycophancy: Negation-Based Gaslighting in Video Large Language ModelsFindings of the Association for Computational LinguisticsZiyao Tang, Pengkun Jiao, Bin Zhu, et al.2026
9. What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM PerformanceFindings of the Association for Computational LinguisticsWilliam Watson, Nicole Cho, Sumitra Ganesh, et al.2026
10. Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed RefinementFindings of the Association for Computational LinguisticsMidan Shim, Seokju Hwang, KaeHyun Um, et al.2026
11. Complexity factors of language models in reading comprehension: a controlled experiment (Étude des facteurs de complexité des modèles de langage dans une tâche de compréhension de lecture à l'aide d'une expérience contrôlée sémantiquement)JEP-TALN-RECITAL (the French-speaking conferences on speech and natural language processing)Elie Antoine, Frederic Bechet, Géraldine Damnati, et al.2024

35 more papers of our library cite it; they have no page here yet.

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