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title: "Who cites A large annotated — page 3 — 2,008 citing papers"
description: "2008 papers of the Elamur Research library cite «A large annotated corpus for learning natural language inference», found in their…"
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# Who cites «A large annotated corpus for learning natural language inference»

2008 papers of our library cite it (of the 149,439 whose bibliographies we read) · 427 with a page here, the most cited first · page 3 of 5 · 201–300 · [← back to «A large annotated corpus for learning natural language inference»](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/)

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201. **[UnNatural Language Inference](https://md.research.elamur.ai/free/en/papers/sinha-2021-unnatural-language-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Koustuv Sinha, Prasanna Parthasarathi, Joelle Pineau, et al.*2021 · 135 citations*
202. **[Interpretable Neural Predictions with Differentiable Binary Variables](https://md.research.elamur.ai/free/en/papers/bastings-2019-interpretable-neural-predictions-differentiable-binary/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jasmijn Bastings, Wilker Aziz, Ivan Titov*2019 · 135 citations*
203. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches arXiv:1904.01172**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 135 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
204. [**Simple synthetic data reduces sycophancy in large language models arXiv:2308.03958**Jerry Wei, Da Huang, Yifeng Lu, et al.*2023 · 134 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-simple-synthetic-data-reduces-sycophancy/)
205. **[DisSent: Learning Sentence Representations from Explicit Discourse Relations](https://md.research.elamur.ai/free/en/papers/nie-2019-dissent-learning-sentence-representations-explicit/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Allen Nie, Erin Bennett, Noah Goodman*2019 · 134 citations*
206. **[What to Pre-Train on? Efficient Intermediate Task Selection](https://md.research.elamur.ai/free/en/papers/poth-2021-what-pre-train-efficient-intermediate/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Clifton Poth, Jonas Pfeiffer, Andreas Rücklé, et al.*2021 · 132 citations*
207. **[Deep Enhanced Representation for Implicit Discourse Relation Recognition](https://md.research.elamur.ai/free/en/papers/bai-2018-deep-enhanced-representation-implicit-discourse/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Hongxiao Bai, Hai Zhao*2018 · 131 citations*
208. **[Natural Language Reasoning, A Survey](https://md.research.elamur.ai/free/en/papers/yu-2023-natural-language-reasoning-survey/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Fei Yu, Hongbo Zhang, Prayag Tiwari, et al.*2023 · 130 citations*
209. [**An Analysis of Natural Language Inference Benchmarks through the Lens of Negation**Md Mosharaf Hossain, Venelin Kovatchev, Pranoy Dutta, et al.*2020 · 130 citations*](https://md.research.elamur.ai/free/en/papers/hossain-2020-analysis-natural-language-inference-benchmarks/)
210. **[The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations](https://md.research.elamur.ai/free/en/papers/nangia-2017-repeval-2017-shared-task-multi/) [Workshop on Evaluating Vector Space Representations for NLP](https://md.research.elamur.ai/free/en/venues/workshop-on-evaluating-vector-space-representations-for-nlp/)**Nikita Nangia, Adina Williams, Angeliki Lazaridou, et al.*2017 · 128 citations*
211. [**Nomic Embed: Training a Reproducible Long Context Text Embedder arXiv:2402.01613**Zach Nussbaum, John X. Morris, Brandon Duderstadt, et al.*2024 · 126 citations*](https://md.research.elamur.ai/free/en/papers/nussbaum-2024-nomic-embed-training-reproducible-long/)
212. **[Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?](https://md.research.elamur.ai/free/en/papers/hase-2020-leakage-adjusted-simulatability-can-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Peter Hase, Shiyue Zhang, Harry Xie, et al.*2020 · 126 citations*
213. **[Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment](https://md.research.elamur.ai/free/en/papers/tsuchiya-2018-performance-impact-caused-hidden-bias/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Masatoshi Tsuchiya*2018 · 126 citations*
214. [**Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation**Atticus Geiger, Kyle Richardson, Christopher Potts*2020 · 123 citations*](https://md.research.elamur.ai/free/en/papers/geiger-2020-neural-natural-language-inference-models/)
215. **[Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/belinkov-2019-don-t-take-premise-granted/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yonatan Belinkov, Adam Poliak, Stuart Shieber, et al.*2019 · 123 citations*
216. [**EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference**Abhilasha Ravichander, Aakanksha Naik, Carolyn Rose, et al.*2019 · 123 citations*](https://md.research.elamur.ai/free/en/papers/ravichander-2019-equate-benchmark-evaluation-framework-quantitative/)
217. **[DocNLI: A Large-scale Dataset for Document-level Natural Language Inference](https://md.research.elamur.ai/free/en/papers/yin-2021-docnli-large-scale-dataset-document/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenpeng Yin, Dragomir Radev, Caiming Xiong*2021 · 121 citations*
218. **[Analyzing Compositionality-Sensitivity of NLI Models](https://md.research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-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/)**Yixin Nie, Yicheng Wang, Mohit Bansal*2018 · 121 citations*
219. **[Sarcasm Analysis Using Conversation Context](https://md.research.elamur.ai/free/en/papers/ghosh-2018-sarcasm-analysis-conversation-context/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Debanjan Ghosh, Alexander R. Fabbri, Smaranda Muresan*2018 · 119 citations*
220. **[MLSUM: The Multilingual Summarization Corpus](https://md.research.elamur.ai/free/en/papers/scialom-2020-mlsum-multilingual-summarization-corpus/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, et al.*2020 · 115 citations*
221. **[WiCE: Real-World Entailment for Claims in Wikipedia](https://md.research.elamur.ai/free/en/papers/kamoi-2023-wice-real-world-entailment-claims/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez, et al.*2023 · 113 citations*
222. [**Reframing Human-AI Collaboration for Generating Free-Text Explanations arXiv:2112.08674**Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, et al.*2021 · 111 citations*](https://md.research.elamur.ai/free/en/papers/wiegreffe-2021-reframing-human-ai-collaboration-generating/)
223. **[What's in the Box? An Analysis of Undesirable Content in the Common Crawl Corpus](https://md.research.elamur.ai/free/en/papers/luccioni-2021-what-s-box-analysis-undesirable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alexandra Luccioni, Joseph Viviano*2021 · 111 citations*
224. **[End-to-End Bias Mitigation by Modelling Biases in Corpora](https://md.research.elamur.ai/free/en/papers/mahabadi-2020-end-end-bias-mitigation-modelling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rabeeh Karimi Mahabadi, Yonatan Belinkov, James Henderson*2020 · 111 citations*
225. **[We're Afraid Language Models Aren't Modeling Ambiguity](https://md.research.elamur.ai/free/en/papers/liu-2023-we-re-afraid-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alisa Liu, Zhaofeng Wu, Julian Michael, et al.*2023 · 110 citations*
226. [**Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework**Aaron Steven White, Pushpendre Rastogi, Kevin Duh, et al.*2017 · 110 citations*](https://md.research.elamur.ai/free/en/papers/white-2017-inference-is-everything-recasting-semantic/)
227. **[Uncertain Natural Language Inference](https://md.research.elamur.ai/free/en/papers/chen-2020-uncertain-natural-language-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tongfei Chen, Zhengping Jiang, Adam Poliak, et al.*2020 · 107 citations*
228. **[Learning to Retrieve In-Context Examples for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-learning-retrieve-context-examples-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Liang Wang, Nan Yang, Furu Wei*2024 · 106 citations*
229. **[Investigating Reasons for Disagreement in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/jiang-2022-investigating-reasons-disagreement-natural-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Nan-Jiang Jiang, Marie-Catherine de Marneffe*2022 · 104 citations*
230. [**AnglE-optimized Text Embeddings arXiv:2309.12871**Xianming Li, Jing Li*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-angle-optimized-text-embeddings/)
231. **[What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning](https://md.research.elamur.ai/free/en/papers/pan-2023-what-context-learning-learns-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jane Pan, Tianyu Gao, Howard Chen, et al.*2023 · 102 citations*
232. **[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*
233. **[Certified Robustness to Text Adversarial Attacks by Randomized MASK](https://md.research.elamur.ai/free/en/papers/zeng-2023-certified-robustness-text-adversarial-attacks/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Jiehang Zeng, Jianhan Xu, Xiaoqing Zheng, et al.*2023 · 101 citations*
234. **[I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling](https://md.research.elamur.ai/free/en/papers/nie-2021-i-like-fish-especially-dolphins/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Nie, Mary Williamson, Mohit Bansal, et al.*2021 · 101 citations*
235. **[Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets](https://md.research.elamur.ai/free/en/papers/wu-2022-generating-data-mitigate-spurious-correlations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxiang Wu, Matt Gardner, Pontus Stenetorp, et al.*2022 · 99 citations*
236. **[Understanding by Understanding Not: Modeling Negation in Language Models](https://md.research.elamur.ai/free/en/papers/hosseini-2021-understanding-understanding-not-modeling-negation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, et al.*2021 · 98 citations*
237. [**Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge arXiv:2006.06609**Alon Talmor, Oyvind Tafjord, Peter Clark, et al.*2020 · 98 citations*](https://md.research.elamur.ai/free/en/papers/talmor-2020-leap-thought-teaching-pre-trained/)
238. **[Faithfulness Tests for Natural Language Explanations](https://md.research.elamur.ai/free/en/papers/atanasova-2023-faithfulness-tests-natural-language-explanations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, et al.*2023 · 96 citations*
239. **[On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/belinkov-2019-adversarial-removal-hypothesis-only-bias/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Yonatan Belinkov, Adam Poliak, Stuart Shieber, et al.*2019 · 96 citations*
240. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
241. **[Tailor: Generating and Perturbing Text with Semantic Controls](https://md.research.elamur.ai/free/en/papers/ross-2022-tailor-generating-perturbing-text-semantic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alexis Ross, Tongshuang Wu, Hao Peng, et al.*2022 · 93 citations*
242. **[CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense](https://md.research.elamur.ai/free/en/papers/chen-2019-codah-adversarially-authored-question-answering/) [Workshop on Evaluating Vector Space Representations for NLP](https://md.research.elamur.ai/free/en/venues/workshop-on-evaluating-vector-space-representations-for-nlp/)**Michael Chen, Mike D'Arcy, Alisa Liu, et al.*2019 · 90 citations*
243. **[LAMBADA: Backward Chaining for Automated Reasoning in Natural Language](https://md.research.elamur.ai/free/en/papers/kazemi-2023-lambada-backward-chaining-automated-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mehran Kazemi, Najoung Kim, Deepti Bhatia, et al.*2023 · 89 citations*
244. **[First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT](https://md.research.elamur.ai/free/en/papers/muller-2021-first-align-then-predict-understanding/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Benjamin Muller, Yanai Elazar, Benoît Sagot, et al.*2021 · 87 citations*
245. **[Back to the Future: Unsupervised Backprop-based Decoding for Counterfactual and Abductive Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/qin-2020-back-future-unsupervised-backprop-based/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lianhui Qin, Vered Shwartz, Peter West, et al.*2020 · 86 citations*
246. **[R^3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge](https://md.research.elamur.ai/free/en/papers/chakrabarty-2020-r-textasciicircum3-reverse-retrieve-rank/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tuhin Chakrabarty, Debanjan Ghosh, Smaranda Muresan, et al.*2020 · 86 citations*
247. **[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/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Thinh Hung Truong, Timothy Baldwin, Karin Verspoor, et al.*2023 · 78 citations*
248. **[Analyzing Dataset Annotation Quality Management in the Wild](https://md.research.elamur.ai/free/en/papers/klie-2024-analyzing-dataset-annotation-quality-management/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Jan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych*2024 · 76 citations*
249. [**Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents arXiv:2310.19923**Michael Gunther, Jackmin Ong, Isabelle Mohr, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/gunther-2023-jina-embeddings-2-8192-token/)
250. [**Gecko: Versatile Text Embeddings Distilled from Large Language Models arXiv:2403.20327**Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, et al.*2024 · 74 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-gecko-versatile-text-embeddings-distilled/)
251. **[ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness](https://md.research.elamur.ai/free/en/papers/prasad-2023-receval-evaluating-reasoning-chains-correctness/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Archiki Prasad, Swarnadeep Saha, Xiang Zhou, et al.*2023 · 73 citations*
252. **[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*
253. **[IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models](https://md.research.elamur.ai/free/en/papers/adelani-2025-irokobench-new-benchmark-african-languages/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**David Ifeoluwa Adelani, Jessica Ojo, Israel Abebe Azime, et al.*2025 · 70 citations*
254. **[Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback](https://md.research.elamur.ai/free/en/papers/roit-2023-factually-consistent-summarization-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Paul Roit, Johan Ferret, Lior Shani, et al.*2023 · 70 citations*
255. **[Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training](https://md.research.elamur.ai/free/en/papers/stacey-2020-avoiding-hypothesis-only-bias-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Joe Stacey, Pasquale Minervini, Haim Dubossarsky, et al.*2020 · 70 citations*
256. **[English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too](https://md.research.elamur.ai/free/en/papers/phang-2020-english-intermediate-task-training-improves/) [Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)](https://md.research.elamur.ai/free/en/venues/aacl/)**Jason Phang, Iacer Calixto, Phu Mon Htut, et al.*2020 · 70 citations*
257. **[Towards Robustifying NLI Models Against Lexical Dataset Biases](https://md.research.elamur.ai/free/en/papers/zhou-2020-robustifying-nli-models-against-lexical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiang Zhou, Mohit Bansal*2020 · 70 citations*
258. [**The Turking Test: Can Language Models Understand Instructions? arXiv:2010.11982**Avia Efrat, Omer Levy*2020 · 69 citations*](https://md.research.elamur.ai/free/en/papers/efrat-2020-turking-test-language-models-understand/)
259. [**IMPLI: Investigating NLI Models' Performance on Figurative Language**Kevin Stowe, Prasetya Utama, Iryna Gurevych*2022 · 68 citations*](https://md.research.elamur.ai/free/en/papers/stowe-2022-impli-investigating-nli-models-performance/)
260. [**VALUE: Understanding Dialect Disparity in NLU**Caleb Ziems, Jiaao Chen, Camille Harris, et al.*2022 · 67 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2022-value-understanding-dialect-disparity-nlu/)
261. **[ParsiNLU: A Suite of Language Understanding Challenges for Persian](https://md.research.elamur.ai/free/en/papers/khashabi-2021-parsinlu-suite-language-understanding-challenges/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Daniel Khashabi, Arman Cohan, Siamak Shakeri, et al.*2021 · 65 citations*
262. **[Figurative Language in Recognizing Textual Entailment](https://md.research.elamur.ai/free/en/papers/chakrabarty-2021-figurative-language-recognizing-textual-entailment/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tuhin Chakrabarty, Debanjan Ghosh, Adam Poliak, et al.*2021 · 63 citations*
263. **[Culturally Aware Natural Language Inference](https://md.research.elamur.ai/free/en/papers/huang-2023-culturally-aware-natural-language-inference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jing Huang, Diyi Yang*2023 · 61 citations*
264. [**How well do NLI models capture verb veridicality?**Alexis Ross, Ellie Pavlick*2019 · 61 citations*](https://md.research.elamur.ai/free/en/papers/ross-2019-how-well-do-nli-models/)
265. **[Posing Fair Generalization Tasks for Natural Language Inference](https://md.research.elamur.ai/free/en/papers/geiger-2019-posing-fair-generalization-tasks-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Atticus Geiger, Ignacio Cases, Lauri Karttunen, et al.*2019 · 61 citations*
266. [**Temporal Reasoning in Natural Language Inference**Siddharth Vashishtha, Adam Poliak, Yash Kumar Lal, et al.*2020 · 59 citations*](https://md.research.elamur.ai/free/en/papers/vashishtha-2020-temporal-reasoning-natural-language-inference/)
267. **[Bias in Large Language Models: Origin, Evaluation, and Mitigation](https://md.research.elamur.ai/free/en/papers/guo-2024-bias-large-language-models-origin/) [Electronics](https://md.research.elamur.ai/free/en/venues/electronics/)**Yufei Guo, Muzhe Guo, Juntao Su, et al.*2024 · 58 citations*
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