--- title: "Who cites Language models are not — 46 citing papers" description: "46 papers of the Elamur Research library cite «Language models are not naysayers: An analysis of language models on negation…" canonical: "https://research.elamur.ai/free/en/papers/truong-2023-language-models-naysayers-analysis-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/truong-2023-language-models-naysayers-analysis-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Language models are not naysayers: An analysis of…](https://md.research.elamur.ai/free/en/papers/truong-2023-language-models-naysayers-analysis-language/) / cited by # 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»](https://md.research.elamur.ai/free/en/papers/truong-2023-language-models-naysayers-analysis-language/) 1. **[ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://md.research.elamur.ai/free/en/papers/koto-2024-arabicmmlu-assessing-massive-multitask-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fajri Koto, Haonan Li, Sara Shatnawi, et al.*2024 · 117 citations* 2. [**Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey arXiv:2404.01869**Philipp Mondorf, Barbara Plank*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/) 3. [**Experimental Pragmatics with Machines: Testing LLM Predictions for the Inferences of Plain and Embedded Disjunctions arXiv:2405.05776**Polina Tsvilodub, Paul Marty, Sonia Ramotowska, et al.*2024 · 3 citations*](https://md.research.elamur.ai/free/en/papers/tsvilodub-2024-experimental-pragmatics-machines-testing-llm/) 4. **[Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understanding](https://md.research.elamur.ai/free/en/papers/so-2026-thunder-nubench-benchmark-llms-sentence/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yeonkyoung 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 Language](https://md.research.elamur.ai/free/en/papers/owers-2026-as-easy-as-rocket-science/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jasmine Owers, Edwin Simpson, Martha Lewis*2026* 6. **[JSTS-Neg: Japanese Semantic Textual Similarity Dataset for Evaluating Negation Understanding Ability](https://md.research.elamur.ai/free/en/papers/yuasa-2026-jsts-neg-japanese-semantic-textual/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Reiko Yuasa, Yoshihide Kato, Shigeki Matsubara*2026* 7. **[NegNLI-BR: A Brazilian Portuguese Benchmark for Negation in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/westhelle-2026-negnli-br-brazilian-portuguese-benchmark/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Matheus Westhelle, Viviane Moreira*2026* 8. **[Spatiotemporal Sycophancy: Negation-Based Gaslighting in Video Large Language Models](https://md.research.elamur.ai/free/en/papers/tang-2026-spatiotemporal-sycophancy-negation-based-gaslighting/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ziyao Tang, Pengkun Jiao, Bin Zhu, et al.*2026* 9. **[What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance](https://md.research.elamur.ai/free/en/papers/watson-2026-what-makes-good-query-measuring/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**William 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 Refinement](https://md.research.elamur.ai/free/en/papers/shim-2026-which-bird-does-not-have/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Midan 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)](https://md.research.elamur.ai/free/en/papers/antoine-2024-etude-des-facteurs-de-complexite/) [JEP-TALN-RECITAL (the French-speaking conferences on speech and natural language processing)](https://md.research.elamur.ai/free/en/venues/jeptalnrecital/)**Elie Antoine, Frederic Bechet, Géraldine Damnati, et al.*2024* 35 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/truong-2023-language-models-naysayers-analysis-language/) — Language models are not naysayers: An analysis of language models on negation benchmarks