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title: "Who cites A large annotated — page 4 — 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 4 of 5 · 301–400 · [← 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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301. [**Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models arXiv:2005.14709**Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro*2020 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schlegel-2020-leaderboards-survey-methods-revealing-weaknesses/)
302. **[STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval](https://md.research.elamur.ai/free/en/papers/conforti-2020-stander-expert-annotated-dataset-news/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, et al.*2020 · 22 citations*
303. **[Whodunnit? Crime Drama as a Case for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/frermann-2018-whodunnit-crime-drama-as-case/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Lea Frermann, Shay B. Cohen, Mirella Lapata*2018 · 22 citations*
304. [**NeoBERT: A Next-Generation BERT arXiv:2502.19587**Lola Le Breton, Quentin Fournier, Mariam El Mezouar, et al.*2025 · 20 citations*](https://md.research.elamur.ai/free/en/papers/breton-2025-neobert-next-generation-bert/)
305. **[The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design](https://md.research.elamur.ai/free/en/papers/snegirev-2025-russian-focused-embedders-exploration-rumteb/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Artem Snegirev, Maria Tikhonova, Maksimova Anna, et al.*2025 · 19 citations*
306. **[Do Large Language Models Understand Conversational Implicature- A case study with a Chinese sitcom](https://md.research.elamur.ai/free/en/papers/yue-2024-do-large-language-models-understand/) [Chinese National Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/ccl/)**Shisen Yue, Siyuan Song, Xinyuan Cheng, et al.*2024 · 19 citations*
307. **[Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://md.research.elamur.ai/free/en/papers/pyatkin-2023-design-choices-crowdsourcing-implicit-discourse/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Valentina Pyatkin, Frances Yung, Merel C. J. Scholman, et al.*2023 · 19 citations*
308. **[“Seeing the Big through the Small”: Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?](https://md.research.elamur.ai/free/en/papers/chen-2024-seeing-big-through-small-can/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Beiduo Chen, Xinpeng Wang, Siyao Peng, et al.*2024 · 18 citations*
309. **[English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts](https://md.research.elamur.ai/free/en/papers/barei-ss-2024-english-prompts-better-nli-based/) [ACM Web Conference (WWW)](https://md.research.elamur.ai/free/en/venues/acm-web-conference-www/)**Patrick Bareiß, Roman Klinger, Jeremy Barnes*2024 · 16 citations*
310. **[Enhancing Semantic Similarity Understanding in Arabic NLP with Nested Embedding Learning](https://md.research.elamur.ai/free/en/papers/nacar-2024-enhancing-semantic-similarity-understanding-arabic/) [Studies in computational intelligence](https://md.research.elamur.ai/free/en/venues/studies-in-computational-intelligence/)**Omer Nacar, Anis Koubaa*2024 · 16 citations*
311. **[When data permutations are pathological: the case of neural natural language inference](https://md.research.elamur.ai/free/en/papers/schluter-2018-when-data-permutations-are-pathological/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Natalie Schluter, Daniel Varab*2018 · 16 citations*
312. **[Understanding Figurative Meaning through Explainable Visual Entailment](https://md.research.elamur.ai/free/en/papers/saakyan-2025-understanding-figurative-meaning-through-explainable/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Arkadiy Saakyan, Shreyas Kulkarni, Tuhin Chakrabarty, et al.*2025 · 15 citations*
313. [**Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detection arXiv:2405.02318**Abhinav Lalwani, Tasha Kim, Lovish Chopra, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/lalwani-2024-autoformalizing-natural-language-first-order/)
314. **[DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning](https://md.research.elamur.ai/free/en/papers/li-2023-diplomat-dialogue-dataset-situated-pragmatic/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hengli Li, Song-Chun Zhu, Zilong Zheng*2023 · 15 citations*
315. **[Curing the SICK and Other NLI Maladies](https://md.research.elamur.ai/free/en/papers/kalouli-2023-curing-sick-other-nli-maladies/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Aikaterini-Lida Kalouli, Hai Hu, Alexander F. Webb, et al.*2023 · 14 citations*
316. **[Prompting for explanations improves Adversarial NLI. Is this true? \Yes\ it is \true\ because ıt weakens superficial cues\](https://md.research.elamur.ai/free/en/papers/kavumba-2023-prompting-explanations-improves-adversarial-nli/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Pride Kavumba, Ana Brassard, Benjamin Heinzerling, et al.*2023 · 14 citations*
317. **[Natural Language Inference with Mixed Effects](https://md.research.elamur.ai/free/en/papers/gantt-2020-natural-language-inference-mixed-effects/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**William Gantt, Benjamin Kane, Aaron Steven White*2020 · 14 citations*
318. **[Natural Language Processing RELIES on Linguistics](https://md.research.elamur.ai/free/en/papers/opitz-2025-natural-language-processing-relies-linguistics/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Juri Opitz, Shira Wein, Nathan Schneider*2025 · 13 citations*
319. **[Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/rajaee-2022-looking-overlooked-analysis-word-overlap/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sara Rajaee, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar*2022 · 13 citations*
320. **[Partial-input baselines show that NLI models can ignore context, but they don't.](https://md.research.elamur.ai/free/en/papers/srikanth-2022-partial-input-baselines-show-that/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Neha Srikanth, Rachel Rudinger*2022 · 13 citations*
321. **[Towards the Necessity for Debiasing Natural Language Inference Datasets](https://md.research.elamur.ai/free/en/papers/panenghat-2020-necessity-debiasing-natural-language-inference/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Mithun Paul Panenghat, Sandeep Suntwal, Faiz Rafique, et al.*2020 · 13 citations*
322. [**Non-entailed subsequences as a challenge for natural language inference arXiv:1811.12112**R. Thomas McCoy, Tal Linzen*2018 · 13 citations*](https://md.research.elamur.ai/free/en/papers/mccoy-2018-non-entailed-subsequences-challenge-natural/)
323. **[SR-LLM: Rethinking the Structured Representation in Large Language Model](https://md.research.elamur.ai/free/en/papers/zhang-2025-sr-llm-rethinking-structured-representation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiahuan Zhang, Tianheng Wang, Hanqing Wu, et al.*2025 · 11 citations*
324. **[Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards](https://md.research.elamur.ai/free/en/papers/valentino-2021-do-natural-language-explanations-represent/) [International Conference on Computational Semantics](https://md.research.elamur.ai/free/en/venues/iwcs/)**Marco Valentino, Ian Pratt-Hartmann, André Freitas*2021 · 11 citations*
325. **[Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings](https://md.research.elamur.ai/free/en/papers/schuff-2021-does-external-knowledge-help-explainable/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Hendrik Schuff, Hsiu-Yu Yang, Heike Adel, et al.*2021 · 11 citations*
326. **[What can we learn from Semantic Tagging?](https://md.research.elamur.ai/free/en/papers/abdou-2018-what-can-we-learn-semantic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mostafa Abdou, Artur Kulmizev, Vinit Ravishankar, et al.*2018 · 11 citations*
327. **[How Often Are Errors in Natural Language Reasoning Due to Paraphrastic Variability?](https://md.research.elamur.ai/free/en/papers/srikanth-2024-how-often-are-errors-natural/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Neha Srikanth, Marine Carpuat, Rachel Rudinger*2024 · 10 citations*
328. **[A Generative Approach for Mitigating Structural Biases in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/asael-2022-generative-approach-mitigating-structural-biases/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Dimion Asael, Zachary Ziegler, Yonatan Belinkov*2022 · 10 citations*
329. **[How Fast can BERT Learn Simple Natural Language Inference?](https://md.research.elamur.ai/free/en/papers/lin-2021-how-fast-can-bert-learn/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yi-Chung Lin, Keh-Yih Su*2021 · 10 citations*
330. **[Marked Attribute Bias in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/dawkins-2021-marked-attribute-bias-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hillary Dawkins*2021 · 10 citations*
331. **[A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI](https://md.research.elamur.ai/free/en/papers/chen-2025-rose-any-other-name-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Beiduo Chen, Siyao Peng, Anna Korhonen, et al.*2025 · 9 citations*
332. **[Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model](https://md.research.elamur.ai/free/en/papers/geigle-2025-centurio-drivers-multilingual-ability-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gregor Geigle, Florian Schneider, Carolin Holtermann, et al.*2025 · 9 citations*
333. **[MSciNLI: A Diverse Benchmark for Scientific Natural Language Inference](https://md.research.elamur.ai/free/en/papers/sadat-2024-mscinli-diverse-benchmark-scientific-natural/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Mobashir Sadat, Cornelia Caragea*2024 · 9 citations*
334. **[Culturally-Nuanced Story Generation for Reasoning in Low-Resource Languages: The Case of Javanese and Sundanese](https://md.research.elamur.ai/free/en/papers/pranida-2025-culturally-nuanced-story-generation-reasoning/) [Workshop on Multilingual Representation Learning](https://md.research.elamur.ai/free/en/venues/mrl/)**Salsabila Zahirah Pranida, Rifo Ahmad Genadi, Fajri Koto*2025 · 8 citations*
335. **[More Labels or Cases? Assessing Label Variation in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/gruber-2024-more-labels-or-cases-assessing/) [Workshop on Understanding Implicit and Underspecified Language](https://md.research.elamur.ai/free/en/venues/unimplicit/)**Cornelia Gruber, Katharina Hechinger, Matthias Assenmacher, et al.*2024 · 8 citations*
336. [**SIGA: A Naturalistic NLI Dataset of English Scalar Implicatures with Gradable Adjectives**Rashid Nizamani, Sebastian Schuster, Vera Demberg*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/nizamani-2024-siga-naturalistic-nli-dataset-english/)
337. [**Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation arXiv:2304.12443**Nan-Jiang Jiang, Chenhao Tan, Marie-Catherine de Marneffe*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-understanding-predicting-human-label-variation/)
338. **[Decomposing Natural Logic Inferences for Neural NLI](https://md.research.elamur.ai/free/en/papers/rozanova-2022-decomposing-natural-logic-inferences-neural/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Julia Rozanova, Deborah Ferreira, Mokanarangan Thayaparan, et al.*2022 · 8 citations*
339. [**GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training arXiv:2505.24581**Omer Nacar, Anis Koubaa, Serry Sibaee, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/nacar-2025-gate-general-arabic-text-embedding/)
340. **[Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic](https://md.research.elamur.ai/free/en/papers/weir-2024-enhancing-systematic-decompositional-natural-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nathaniel Weir, Kate Sanders, Orion Weller, et al.*2024 · 7 citations*
341. **[Simple but Challenging: Natural Language Inference Models Fail on Simple Sentences](https://md.research.elamur.ai/free/en/papers/luo-2022-simple-but-challenging-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cheng Luo, Wei Liu, Jieyu Lin, et al.*2022 · 7 citations*
342. [**Investigating Biases in Textual Entailment Datasets arXiv:1906.09635**Shawn Tan, Yikang Shen, Chin-wei Huang, et al.*2019 · 7 citations*](https://md.research.elamur.ai/free/en/papers/tan-2019-investigating-biases-textual-entailment-datasets/)
343. **[Recognizing Textual Entailment in Twitter Using Word Embeddings](https://md.research.elamur.ai/free/en/papers/sulea-2017-recognizing-textual-entailment-twitter-word/) [Workshop on Evaluating Vector Space Representations for NLP](https://md.research.elamur.ai/free/en/venues/workshop-on-evaluating-vector-space-representations-for-nlp/)**Octavia-Maria Şulea*2017 · 7 citations*
344. **[Entailed Between the Lines: Incorporating Implication into NLI](https://md.research.elamur.ai/free/en/papers/havaldar-2025-entailed-between-lines-incorporating-implication/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shreya Havaldar, Hamidreza Alvari, John Palowitch, et al.*2025 · 6 citations*
345. **[Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic IR in English and Arabic](https://md.research.elamur.ai/free/en/papers/pavlova-2023-leveraging-domain-adaptation-data-augmentation/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Vera Pavlova*2023 · 6 citations*
346. **[Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging](https://md.research.elamur.ai/free/en/papers/talman-2023-uncertainty-aware-natural-language-inference/) [Nordic Conference on Computational Linguistics (NoDaLiDa)](https://md.research.elamur.ai/free/en/venues/nodalida/)**Aarne Talman, Hande Celikkanat, Sami Virpioja, et al.*2023 · 6 citations*
347. **[Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference](https://md.research.elamur.ai/free/en/papers/dacon-2022-evaluating-mitigating-inherent-linguistic-bias/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Jamell Dacon, Haochen Liu, Jiliang Tang*2022 · 6 citations*
348. **[Exploring Continual Learning of Compositional Generalization in NLI](https://md.research.elamur.ai/free/en/papers/fu-2024-exploring-continual-learning-compositional-generalization/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Xiyan Fu, Anette Frank*2024 · 5 citations*
349. **[Annotation Difficulties in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/kalouli-2021-annotation-difficulties-natural-language-inference/) [Brazilian Symposium in Information and Human Language Technology](https://md.research.elamur.ai/free/en/venues/stil/)**Aikaterini-Lida Kalouli, Livy Real, Annebeth Buis, et al.*2021 · 5 citations*
350. **[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*
351. **[AfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languages](https://md.research.elamur.ai/free/en/papers/uemura-2026-afrimteb-afrie5-benchmarking-adapting-text/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Kosei Uemura, Miaoran Zhang, David Ifeoluwa Adelani*2026 · 4 citations*
352. **[Evaluating Numeracy of Language Models as a Natural Language Inference Task](https://md.research.elamur.ai/free/en/papers/mahendra-2025-evaluating-numeracy-language-models-as/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rahmad Mahendra, Damiano Spina, Lawrence Cavedon, et al.*2025 · 4 citations*
353. [**Let's CONFER: A Dataset for Evaluating Natural Language Inference Models on CONditional InFERence and Presupposition arXiv:2506.06133**Tara Azin, Daniel Dumitrescu, Diana Inkpen, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/azin-2025-let-s-confer-dataset-evaluating/)
354. **[It is not True that Transformers are Inductive Learners: Probing NLI Models with External Negation](https://md.research.elamur.ai/free/en/papers/sullivan-2024-it-is-not-true-that/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Michael Sullivan*2024 · 4 citations*
355. **[Which Spurious Correlations Impact Reasoning in NLI Models? A Visual Interactive Diagnosis through Data-Constrained Counterfactuals](https://md.research.elamur.ai/free/en/papers/chan-2023-which-spurious-correlations-impact-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Robin Chan, Afra Amini, Mennatallah El-Assady*2023 · 4 citations*
356. **[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*
357. [**A guide to the dataset explosion in QA, NLI, and commonsense reasoning**Anna Rogers, Anna Rumshisky*2020 · 4 citations*](https://md.research.elamur.ai/free/en/papers/rogers-2020-guide-dataset-explosion-qa-nli/)
358. [**How does Punctuation Affect Neural Models in Natural Language Inference Proceedings of the Probability and Meaning Conference (PaM 2020)**Adam Ek, Jean-Philippe Bernardy, Stergios Chatzikyriakidis*2020 · 4 citations*](https://md.research.elamur.ai/free/en/papers/ek-2020-how-does-punctuation-affect-neural/)
359. **[Predicting the Argumenthood of English Prepositional Phrases](https://md.research.elamur.ai/free/en/papers/kim-2018-predicting-argumenthood-english-prepositional-phrases/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Najoung Kim, Kyle Rawlins, Benjamin Van Durme, et al.*2018 · 4 citations*
360. **[Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations](https://md.research.elamur.ai/free/en/papers/hong-2026-agree-disagree-explain-decomposing-human/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Pingjun Hong, Beiduo Chen, Siyao Peng, et al.*2026 · 3 citations*
361. **[FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking](https://md.research.elamur.ai/free/en/papers/magomere-2025-finnli-novel-dataset-multi-genre/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jabez Magomere, Elena Kochkina, Samuel Mensah, et al.*2025 · 3 citations*
362. **[From Disagreement to Understanding: The Case for Ambiguity Detection in NLI](https://md.research.elamur.ai/free/en/papers/jayaweera-2025-disagreement-understanding-case-ambiguity-detection/) [edition of NLPerspectives](https://md.research.elamur.ai/free/en/venues/nlperspectives/)**Chathuri Jayaweera, Bonnie J. Dorr*2025 · 3 citations*
363. **[How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics](https://md.research.elamur.ai/free/en/papers/cosma-2024-how-hard-is-this-test/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Adrian Cosma, Stefan Ruseti, Mihai Dascalu, et al.*2024 · 3 citations*
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