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Who cites «Annotation Artifacts in Natural Language Inference Data»

721 papers of our library cite it (of the 149,439 whose bibliographies we read) · 168 with a page here, the most cited first · page 2 of 2 · 101–168 · ← back to «Annotation Artifacts in Natural Language Inference Data»

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  1. ConjNLI: Natural Language Inference Over Conjunctive SentencesConference on Empirical Methods in Natural Language Processing (EMNLP)Swarnadeep Saha, Yixin Nie, Mohit Bansal2020 · 51 citations
  2. ANLIzing the Adversarial Natural Language Inference DatasetSociety for Computation in Linguistics (SCiL)Adina Williams, Tristan Thrush, Douwe Kiela2022 · 48 citations
  3. SciNLI: A Corpus for Natural Language Inference on Scientific TextAnnual Meeting of the Association for Computational Linguistics (ACL)Mobashir Sadat, Cornelia Caragea2022 · 43 citations
  4. Finding Generalizable Evidence by Learning to Convince Q&A ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Ethan Perez, Siddharth Karamcheti, Rob Fergus, et al.2019 · 43 citations
  5. LitSearch: A Retrieval Benchmark for Scientific Literature SearchConference on Empirical Methods in Natural Language Processing (EMNLP)Anirudh Ajith, Mengzhou Xia, Alexis Chevalier, et al.2024 · 39 citations
  6. An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language InferenceConference on Computational Natural Language Learning (CoNLL)Tianyu Liu, Xin Zheng, Xiaoan Ding, et al.2020 · 37 citations
  7. Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified SentencesarXiv:1810.13033Atticus Geiger; Ignacio Cases; Lauri Karttunen;, Christopher Potts2018 · 37 citations
  8. MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical DomainAnnual Meeting of the Association for Computational Linguistics (ACL)Christine Herlihy, Rachel Rudinger2021 · 30 citations
  9. Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learningConference on Computational Natural Language Learning (CoNLL)Lucas Weber, Elia Bruni, Dieuwke Hupkes2023 · 29 citations
  10. Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the AboveAnnual Meeting of the Association for Computational Linguistics (ACL)Nishant Balepur, Rachel Rudinger, Jordan Boyd-Graber2025 · 28 citations
  11. Context Matters: A Pragmatic Study of PLMs' Negation UnderstandingAnnual Meeting of the Association for Computational Linguistics (ACL)Reto Gubelmann, Siegfried Handschuh2022 · 28 citations
  12. HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language InferenceLanguage Resources and Evaluation Conference (LREC)Tianyu Liu, Xin Zheng, Baobao Chang, et al.2020 · 28 citations
  13. SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language InferenceMartin Schmitt, Hinrich Schütze2019 · 28 citations
  14. Exploring Transitivity in Neural NLI Models through VeridicalityConference of the European Chapter of the Association for Computational Linguistics (EACL)Hitomi Yanaka, Koji Mineshima, Kentaro Inui2021 · 27 citations
  15. New Protocols and Negative Results for Textual Entailment Data CollectionConference on Empirical Methods in Natural Language Processing (EMNLP)Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, et al.2020 · 27 citations
  16. Sensitivity as a Complexity Measure for Sequence Classification TasksTransactions of the Association for Computational Linguistics (TACL)Michael Hahn, Dan Jurafsky, Richard Futrell2021 · 26 citations
  17. Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal NegationConference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)Hung Thinh Truong, Yulia Otmakhova, Timothy Baldwin, et al.2022 · 24 citations
  18. Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and modelsarXiv:2005.14709Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro2020 · 24 citations
  19. Ecologically Valid Explanations for Label Variation in NLIFindings of the Association for Computational LinguisticsNan-Jiang Jiang, Chenhao Tan, Marie-Catherine de Marneffe2023 · 19 citations
  20. Stress Test Evaluation of Transformer-based Models in Natural Language Understanding TasksLanguage Resources and Evaluation Conference (LREC)Carlos Aspillaga, Andrés Carvallo, Vladimir Araujo2020 · 17 citations
  21. Question-Answering in a Low-resourced Language: Benchmark Dataset and Models for TigrinyaAnnual Meeting of the Association for Computational Linguistics (ACL)Fitsum Gaim, Wonsuk Yang, Hancheol Park, et al.2023 · 16 citations
  22. When data permutations are pathological: the case of neural natural language inferenceConference on Empirical Methods in Natural Language Processing (EMNLP)Natalie Schluter, Daniel Varab2018 · 16 citations
  23. Understanding Figurative Meaning through Explainable Visual EntailmentConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Arkadiy Saakyan, Shreyas Kulkarni, Tuhin Chakrabarty, et al.2025 · 15 citations
  24. When Truth Matters - Addressing Pragmatic Categories in Natural Language Inference (NLI) by Large Language Models (LLMs)Joint Conference on Lexical and Computational Semantics (*SEM)Reto Gubelmann, Aikaterini-lida Kalouli, Christina Niklaus, et al.2023 · 15 citations
  25. WorldSense: A Synthetic Benchmark for Grounded Reasoning in Large Language ModelsarXiv:2311.15930Youssef Benchekroun, Megi Dervishi, Mark Ibrahim, et al.2023 · 15 citations
  26. Adversarial Analysis of Natural Language Inference SystemsarXiv:1912.03441Tiffany Chien, Jugal Kalita2019 · 15 citations
  27. Curing the SICK and Other NLI MaladiesComputational LinguisticsAikaterini-Lida Kalouli, Hai Hu, Alexander F. Webb, et al.2023 · 14 citations
  28. Prompting for explanations improves Adversarial NLI. Is this true? \Yes\ it is \true\ because ıt weakens superficial cues\Findings of the Association for Computational LinguisticsPride Kavumba, Ana Brassard, Benjamin Heinzerling, et al.2023 · 14 citations
  29. An Annotated Corpus of Reference Resolution for Interpreting Common GroundingProceedings of the AAAI Conference on Artificial IntelligenceTakuma Udagawa, Akiko Aizawa2019 · 14 citations
  30. Partial-input baselines show that NLI models can ignore context, but they don't.Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)Neha Srikanth, Rachel Rudinger2022 · 13 citations
  31. Towards the Necessity for Debiasing Natural Language Inference DatasetsLanguage Resources and Evaluation Conference (LREC)Mithun Paul Panenghat, Sandeep Suntwal, Faiz Rafique, et al.2020 · 13 citations
  32. Non-entailed subsequences as a challenge for natural language inferencearXiv:1811.12112R. Thomas McCoy, Tal Linzen2018 · 13 citations
  33. How Often Are Errors in Natural Language Reasoning Due to Paraphrastic Variability?Transactions of the Association for Computational Linguistics (TACL)Neha Srikanth, Marine Carpuat, Rachel Rudinger2024 · 10 citations
  34. A Generative Approach for Mitigating Structural Biases in Natural Language InferenceJoint Conference on Lexical and Computational Semantics (*SEM)Dimion Asael, Zachary Ziegler, Yonatan Belinkov2022 · 10 citations
  35. How Fast can BERT Learn Simple Natural Language Inference?Conference of the European Chapter of the Association for Computational Linguistics (EACL)Yi-Chung Lin, Keh-Yih Su2021 · 10 citations
  36. MSciNLI: A Diverse Benchmark for Scientific Natural Language InferenceConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Mobashir Sadat, Cornelia Caragea2024 · 9 citations
  37. PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster OutliersConference of the European Chapter of the Association for Computational Linguistics (EACL)Michael Saxon, Xinyi Wang, Wenda Xu, et al.2023 · 9 citations
  38. Simple but Challenging: Natural Language Inference Models Fail on Simple SentencesFindings of the Association for Computational LinguisticsCheng Luo, Wei Liu, Jieyu Lin, et al.2022 · 7 citations
  39. Investigating Biases in Textual Entailment DatasetsarXiv:1906.09635Shawn Tan, Yikang Shen, Chin-wei Huang, et al.2019 · 7 citations
  40. SSR: Utilizing Simplified Stance Reasoning Process for Robust Stance DetectionInternational Conference on Computational Linguistics (COLING)Jianhua Yuan, Yanyan Zhao, Yanyue Lu, et al.2022 · 6 citations
  41. Annotation Difficulties in Natural Language InferenceBrazilian Symposium in Information and Human Language TechnologyAikaterini-Lida Kalouli, Livy Real, Annebeth Buis, et al.2021 · 5 citations
  42. Evaluating Numeracy of Language Models as a Natural Language Inference TaskFindings of the Association for Computational LinguisticsRahmad Mahendra, Damiano Spina, Lawrence Cavedon, et al.2025 · 4 citations
  43. A guide to the dataset explosion in QA, NLI, and commonsense reasoningAnna Rogers, Anna Rumshisky2020 · 4 citations
  44. FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference BenchmarkingFindings of the Association for Computational LinguisticsJabez Magomere, Elena Kochkina, Samuel Mensah, et al.2025 · 3 citations
  45. NLI under the Microscope: What Atomic Hypothesis Decomposition RevealsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Neha Srikanth, Rachel Rudinger2025 · 3 citations
  46. How Hard is this Test Set? NLI Characterization by Exploiting Training DynamicsConference on Empirical Methods in Natural Language Processing (EMNLP)Adrian Cosma, Stefan Ruseti, Mihai Dascalu, et al.2024 · 3 citations
  47. PROPRES: Investigating the Projectivity of Presupposition with Various Triggers and EnvironmentsConference on Computational Natural Language Learning (CoNLL)Daiki Asami, Saku Sugawara2023 · 3 citations
  48. Improving the Precision of Natural Textual Entailment Problem DatasetsLanguage Resources and Evaluation Conference (LREC)Jean-Philippe Bernardy, Stergios Chatzikyriakidis2020 · 3 citations
  49. Hypothesis-only Biases in Large Language Model-Elicited Natural Language InferencearXiv:2410.08996Grace Proebsting, Adam Poliak2024 · 2 citations
  50. Uncovering More Shallow Heuristics: Probing the Natural Language Inference Capacities of Transformer-Based Pre-Trained Language Models Using Syllogistic PatternsarXiv:2201.07614Reto Gubelmann, Siegfried Handschuh2022 · 2 citations
  51. Understanding Roles and Entities: Datasets and Models for Natural Language InferencearXiv:1904.09720Arindam Mitra, Ishan Shrivastava, Chitta Baral2019 · 2 citations
  52. Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data SelectionFindings of the Association for Computational LinguisticsJoe Stacey, Lisa Alazraki, Aran Ubhi, et al.2026 · 1 citation
  53. SynthLLM: An LLM-based Scalable Synthetic Data Generation Pipeline for Low-Resource LanguagesLanguage Resources and Evaluation Conference (LREC)Solmaz Panahi, Vasudevan Nedumpozhimana, John Kelleher2026 · 1 citation
  54. Towards a Diagnostic and Predictive Evaluation Methodology for Sequence Labeling TasksLanguage Resources and Evaluation Conference (LREC)Elena Alvarez-Mellado, Julio Gonzalo2026 · 1 citation
  55. Biases in Large Language Model-Elicited Text: A Case Study in Natural Language InferenceInternational Conference on Computational Linguistics (COLING)Grace Proebsting, Adam Poliak2025 · 1 citation
  56. From Form to Function: A Constructional NLI BenchmarkInternational Workshop on Construction Grammars and NLPClaire Bonial, Taylor Pellegrin, Melissa Torgbi, et al.2025 · 1 citation
  57. On Reference (In-)Determinacy in Natural Language InferenceFindings of the Association for Computational LinguisticsSihao Chen, Chaitanya Malaviya, Alex Fabrikant, et al.2025 · 1 citation
  58. Exploiting Open IE for Deriving Multiple Premises Entailment CorpusRecent Advances in Natural Language Processing (RANLP)Martin Víta, Jakub Klímek2019 · 1 citation
  59. Annotation Entropy Predicts Per-Example Learning Dynamics in LoRA Fine-TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Brady Steele2026
  60. Are Arabic Benchmarks Reliable? QIMMA's Quality-First Approach to LLM EvaluationarXiv:2604.03395Leen AlQadi, Ahmed Alzubaidi, Mohammed Alyafeai, et al.2026
  61. Assessing the Difficulty of Inference Types in Natural Language Inference for Clinical TrialsLanguage Resources and Evaluation Conference (LREC)Mathilde Aguiar, Pierre Zweigenbaum, Nona Naderi2026
  62. CxMP: A Linguistic Minimal-Pair Benchmark for Evaluating Constructional Understanding in Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Miyu Oba, Saku Sugawara2026
  63. Gold Label Errors in the SciFact Benchmark: An LLM-Assisted Annotation AuditBioNLPJulien Sylvestre2026
  64. Large Language Models Are Still Misled by Simple Bias EnsemblesFindings of the Association for Computational LinguisticsZhouhao Sun, Zhiyuan Kan, Xiao Ding, et al.2026
  65. Measuring and Mitigating Shortcut Reliance in Language Models with Probe-Based Representation EntanglementAnnual Meeting of the Association for Computational Linguistics (ACL)Divyajot Singh2026
  66. Pushing the Frontiers of Scientific Fact-Checking: The SCINLP DatasetFindings of the Association for Computational LinguisticsIffat Maab, Junichi Yamagishi2026
  67. Word Surprisal Correlates with Sentential Contradiction in LLMsConference of the European Chapter of the Association for Computational Linguistics (EACL)Ning Shi, Bradley Hauer, David Basil, et al.2026
  68. When Does Meaning Backfire? Investigating the Role of AMRs in NLIJoint Conference on Lexical and Computational Semantics (*SEM)Junghyun Min, Xiulin Yang, Shira Wein2025

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