--- title: "Who cites Inoculation by Fine-Tuning: A — 60 citing papers" description: "60 papers of the Elamur Research library cite «Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets», found in their…" canonical: "https://research.elamur.ai/free/en/papers/liu-2019-inoculation-fine-tuning-method-analyzing/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/liu-2019-inoculation-fine-tuning-method-analyzing/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Inoculation by Fine-Tuning: A Method for…](https://md.research.elamur.ai/free/en/papers/liu-2019-inoculation-fine-tuning-method-analyzing/) / cited by # Who cites «Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets» 60 papers of our library cite it (of the 141,840 whose bibliographies we read) · 20 with a page here, the most cited first · [← back to «Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets»](https://md.research.elamur.ai/free/en/papers/liu-2019-inoculation-fine-tuning-method-analyzing/) 1. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems arXiv:1905.00537**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,190 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/) **[Pretrained Transformers Improve Out-of-Distribution Robustness](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-pretrained-transformers-improve-out-distribution/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, et al.*2020 · 443 citations* **[Certified Robustness to Adversarial Word Substitutions](https://md.research.elamur.ai/free/en/papers/jia-2019-certified-robustness-adversarial-word-substitutions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Robin Jia, Aditi Raghunathan, Kerem Göksel, et al.*2019 · 337 citations* **[How Can We Accelerate Progress Towards Human-like Linguistic Generalization?](https://md.research.elamur.ai/free/en/papers/linzen-2020-how-can-we-accelerate-progress/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tal Linzen*2020 · 258 citations* [**Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning**Pradeep Dasigi, Nelson F. Liu, Ana Marasovic, et al.*2019 · 258 citations*](https://md.research.elamur.ai/free/en/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/) **[Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual](https://md.research.elamur.ai/free/en/papers/he-2019-unlearn-dataset-bias-natural-language/) [Workshop on Deep Learning Approaches for Low-Resource NLP](https://md.research.elamur.ai/free/en/venues/workshop-on-deep-learning-approaches-for-low-resource-nlp/)**He He, Sheng Zha, Haohan Wang*2019 · 254 citations* **[Syntactic Data Augmentation Increases Robustness to Inference Heuristics](https://md.research.elamur.ai/free/en/papers/min-2020-syntactic-data-augmentation-increases-robustness/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junghyun Min, R. Thomas McCoy, Dipanjan Das, et al.*2020 · 217 citations* **[HateCheck: Functional Tests for Hate Speech Detection Models](https://md.research.elamur.ai/free/en/papers/rottger-2021-hatecheck-functional-tests-hate-speech/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Paul Röttger, Bertie Vidgen, Dong Nguyen, et al.*2021 · 203 citations* **[Measure and Improve Robustness in NLP Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2022-measure-improve-robustness-nlp-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xuezhi Wang, Haohan Wang, Diyi Yang*2022 · 148 citations* **[Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?](https://md.research.elamur.ai/free/en/papers/ravichander-2021-probing-probing-paradigm-does-probing/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Abhilasha Ravichander, Yonatan Belinkov, Eduard Hovy*2021 · 141 citations* [**Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation**Atticus Geiger, Kyle Richardson, Christopher Potts*2020 · 112 citations*](https://md.research.elamur.ai/free/en/papers/geiger-2020-neural-natural-language-inference-models/) **[Probing Natural Language Inference Models through Semantic Fragments](https://md.research.elamur.ai/free/en/papers/richardson-2019-probing-natural-language-inference-models/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Kyle Richardson, Hai Hu, Lawrence S. Moss, et al.*2019 · 102 citations* **[Evaluating BERT for natural language inference: A case study on the CommitmentBank](https://md.research.elamur.ai/free/en/papers/jiang-2019-evaluating-bert-natural-language-inference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nanjiang Jiang, Marie-Catherine de Marneffe*2019 · 72 citations* **[ConjNLI: Natural Language Inference Over Conjunctive Sentences](https://md.research.elamur.ai/free/en/papers/saha-2020-conjnli-natural-language-inference-over/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Swarnadeep Saha, Yixin Nie, Mohit Bansal*2020 · 50 citations* **[CxGBERT: BERT meets Construction Grammar](https://md.research.elamur.ai/free/en/papers/madabushi-2020-cxgbert-bert-meets-construction-grammar/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Harish Tayyar Madabushi, Laurence Romain, Dagmar Divjak, et al.*2020 · 46 citations* **[Exploring Transitivity in Neural NLI Models through Veridicality](https://md.research.elamur.ai/free/en/papers/yanaka-2021-exploring-transitivity-neural-nli-models/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Hitomi Yanaka, Koji Mineshima, Kentaro Inui*2021 · 27 citations* [**Adversarial Analysis of Natural Language Inference Systems arXiv:1912.03441**Tiffany Chien, Jugal Kalita*2019 · 15 citations*](https://md.research.elamur.ai/free/en/papers/chien-2019-adversarial-analysis-natural-language-inference/) **[Supporting Context Monotonicity Abstractions in Neural NLI Models](https://md.research.elamur.ai/free/en/papers/rozanova-2021-supporting-context-monotonicity-abstractions-neural/) [Workshops on Natural Logic Meets Machine Learning (NALOMA)](https://md.research.elamur.ai/free/en/venues/naloma/)**Julia Rozanova, Deborah Ferreira, Mokanarangan Thayaparan, et al.*2021 · 5 citations* **[FANCY: A Diagnostic Data-Set for NLI Models](https://md.research.elamur.ai/free/en/papers/rocchietti-2021-fancy-diagnostic-data-set-nli/) [Italian Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/clicit/)**Guido Rocchietti, Flavia Achena, Giuseppe Marziano, et al.*2021 · 4 citations* **[Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization](https://md.research.elamur.ai/free/en/papers/mersinias-2022-mitigating-dataset-artifacts-natural-language/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Michail Mersinias, Panagiotis Valvis*2022 · 3 citations* 40 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/liu-2019-inoculation-fine-tuning-method-analyzing/) — Inoculation by Fine-Tuning: A Method for Analyzing Challenge Datasets