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title: "Who cites A Broad-Coverage — page 4 — 2,320 citing papers"
description: "2,320 papers of the Elamur Research library cite «A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference»…"
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# Who cites «A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference»

2,320 papers of our library cite it (of the 149,439 whose bibliographies we read) · 483 with a page here, the most cited first · page 4 of 5 · 301–400 · [← back to «A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference»](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/)

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301. [**Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering**Najoung Kim, Ellie Pavlick, Burcu Karagol Ayan, et al.*2021 · 63 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-which-linguist-invented-lightbulb-presupposition/)
302. [**Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves arXiv:2311.04205**Yihe Deng, Weitong Zhang, Zixiang Chen, et al.*2023 · 62 citations*](https://md.research.elamur.ai/free/en/papers/deng-2023-rephrase-respond-let-large-language/)
303. [**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/)
304. [**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/)
305. [**SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark arXiv:2307.15020**Liang Xu, Anqi Li, Lei Zhu, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-superclue-comprehensive-chinese-large/)
306. **[On the Systematicity of Probing Contextualized Word Representations: The Case of Hypernymy in BERT](https://md.research.elamur.ai/free/en/papers/ravichander-2020-systematicity-probing-contextualized-word-representations/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Abhilasha Ravichander, Eduard Hovy, Kaheer Suleman, et al.*2020 · 55 citations*
307. **[A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains](https://md.research.elamur.ai/free/en/papers/jacovi-2024-chain-thought-is-as-strong/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alon Jacovi, Yonatan Bitton, Bernd Bohnet, et al.*2024 · 54 citations*
308. [**In-context Learning with Retrieved Demonstrations for Language Models: A Survey arXiv:2401.11624**Man Luo, Xin Xu, Yue Liu, et al.*2024 · 54 citations*](https://md.research.elamur.ai/free/en/papers/luo-2024-context-learning-retrieved-demonstrations-language/)
309. [**FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark arXiv:2107.07498**Liang Xu, Xiaojing Lu, Chenyang Yuan, et al.*2021 · 54 citations*](https://md.research.elamur.ai/free/en/papers/xu-2021-fewclue-chinese-few-shot/)
310. [**Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners arXiv:2305.14825**Xiaojuan Tang, Zilong Zheng, Jiaqi Li, et al.*2023 · 52 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-large-language-models-context-semantic/)
311. **[VariErr NLI: Separating Annotation Error from Human Label Variation](https://md.research.elamur.ai/free/en/papers/genzel-2024-varierr-nli-separating-annotation-error/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leon Weber-Genzel, Siyao Peng, Marie-Catherine De Marneffe, et al.*2024 · 51 citations*
312. **[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 · 51 citations*
313. **[Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data](https://md.research.elamur.ai/free/en/papers/huang-2020-counterfactually-augmented-snli-training-data/) [Workshop on Insights from Negative Results in NLP](https://md.research.elamur.ai/free/en/venues/insights/)**William Huang, Haokun Liu, Samuel R. Bowman*2020 · 51 citations*
314. **[ANLIzing the Adversarial Natural Language Inference Dataset](https://md.research.elamur.ai/free/en/papers/williams-2022-anlizing-adversarial-natural-language-inference/) [Society for Computation in Linguistics (SCiL)](https://md.research.elamur.ai/free/en/venues/scil/)**Adina Williams, Tristan Thrush, Douwe Kiela*2022 · 48 citations*
315. **[ALUE: Arabic Language Understanding Evaluation](https://md.research.elamur.ai/free/en/papers/seelawi-2021-alue-arabic-language-understanding-evaluation/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Haitham Seelawi, Ibraheem Tuffaha, Mahmoud Gzawi, et al.*2021 · 48 citations*
316. [**Understanding Catastrophic Forgetting in Language Models via Implicit Inference arXiv:2309.10105**Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/kotha-2023-understanding-catastrophic-forgetting-language-models/)
317. [**Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models arXiv:2308.11764**Mohamed Elaraby, Mengyin Lu, Jacob Dunn, et al.*2023 · 43 citations*](https://md.research.elamur.ai/free/en/papers/elaraby-2023-halo-estimation-reduction-hallucinations-open/)
318. **[SciNLI: A Corpus for Natural Language Inference on Scientific Text](https://md.research.elamur.ai/free/en/papers/sadat-2022-scinli-corpus-natural-language-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mobashir Sadat, Cornelia Caragea*2022 · 43 citations*
319. **[AmbiFC: Fact-Checking Ambiguous Claims with Evidence](https://md.research.elamur.ai/free/en/papers/glockner-2024-ambifc-fact-checking-ambiguous-claims/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Max Glockner, Ieva Staliūnaitė, James Thorne, et al.*2024 · 42 citations*
320. **[LUQ: Long-text Uncertainty Quantification for LLMs](https://md.research.elamur.ai/free/en/papers/zhang-2024-luq-long-text-uncertainty-quantification/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Caiqi Zhang, Fangyu Liu, Marco Basaldella, et al.*2024 · 42 citations*
321. [**Goal Driven Discovery of Distributional Differences via Language Descriptions arXiv:2302.14233**Ruiqi Zhong, Peter Zhang, Steve Li, et al.*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2023-goal-driven-discovery-distributional-differences/)
322. **[EPIC: Multi-Perspective Annotation of a Corpus of Irony](https://md.research.elamur.ai/free/en/papers/frenda-2023-epic-multi-perspective-annotation-corpus/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Simona Frenda, Alessandro Pedrani, Valerio Basile, et al.*2023 · 41 citations*
323. **[A Systematic Survey of Automatic Prompt Optimization Techniques](https://md.research.elamur.ai/free/en/papers/ramnath-2025-systematic-survey-automatic-prompt-optimization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kiran Ramnath, Kang Zhou, Sheng Guan, et al.*2025 · 40 citations*
324. [**NOPE: A Corpus of Naturally-Occurring Presuppositions in English**Alicia Parrish, Sebastian Schuster, Alex Warstadt, et al.*2021 · 40 citations*](https://md.research.elamur.ai/free/en/papers/parrish-2021-nope-corpus-naturally-occurring-presuppositions/)
325. **[Identifying inherent disagreement in natural language inference](https://md.research.elamur.ai/free/en/papers/zhang-2021-identifying-inherent-disagreement-natural-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xinliang Frederick Zhang, Marie-Catherine de Marneffe*2021 · 38 citations*
326. **[An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference](https://md.research.elamur.ai/free/en/papers/liu-2020-empirical-study-model-agnostic-debiasing/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Tianyu Liu, Xin Zheng, Xiaoan Ding, et al.*2020 · 37 citations*
327. [**Stress-Testing Neural Models of Natural Language Inference with Multiply-Quantified Sentences arXiv:1810.13033**Atticus Geiger; Ignacio Cases; Lauri Karttunen;, Christopher Potts*2018 · 37 citations*](https://md.research.elamur.ai/free/en/papers/geiger-2018-stress-testing-neural-models-natural/)
328. **[Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning](https://md.research.elamur.ai/free/en/papers/zhou-2022-distributed-nli-learning-predict-human/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiang Zhou, Yixin Nie, Mohit Bansal*2022 · 36 citations*
329. **[LINSPECTOR: Multilingual Probing Tasks for Word Representations](https://md.research.elamur.ai/free/en/papers/sahin-2020-linspector-multilingual-probing-tasks-word/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Gözde Gül Şahin, Clara Vania, Ilia Kuznetsov, et al.*2020 · 35 citations*
330. [**EuroBERT: Scaling Multilingual Encoders for European Languages arXiv:2503.05500**Nicolas Boizard, Hippolyte Gisserot-Boukhlef, Duarte M. Alves, et al.*2025 · 34 citations*](https://md.research.elamur.ai/free/en/papers/boizard-2025-eurobert-scaling-multilingual-encoders-european/)
331. **[Compositional Evaluation on Japanese Textual Entailment and Similarity](https://md.research.elamur.ai/free/en/papers/yanaka-2022-compositional-evaluation-japanese-textual-entailment/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Hitomi Yanaka, Koji Mineshima*2022 · 33 citations*
332. [**Interpretable Unified Language Checking arXiv:2304.03728**Tianhua Zhang, Hongyin Luo, Yung-Sung Chuang, et al.*2023 · 32 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-interpretable-unified-language-checking/)
333. **[OpenStance: Real-world Zero-shot Stance Detection](https://md.research.elamur.ai/free/en/papers/xu-2022-openstance-real-world-zero-shot/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Hanzi Xu, Slobodan Vucetic, Wenpeng Yin*2022 · 32 citations*
334. **[PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition](https://md.research.elamur.ai/free/en/papers/chen-2023-propsegment-large-scale-corpus-proposition/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sihao Chen, Senaka Buthpitiya, Alex Fabrikant, et al.*2023 · 31 citations*
335. **[MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain](https://md.research.elamur.ai/free/en/papers/herlihy-2021-mednli-is-not-immune-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Christine Herlihy, Rachel Rudinger*2021 · 30 citations*
336. **[Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning](https://md.research.elamur.ai/free/en/papers/weber-2023-mind-instructions-holistic-evaluation-consistency/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Lucas Weber, Elia Bruni, Dieuwke Hupkes*2023 · 29 citations*
337. **[HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/liu-2020-hyponli-exploring-artificial-patterns-hypothesis/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Tianyu Liu, Xin Zheng, Baobao Chang, et al.*2020 · 28 citations*
338. [**SherLIiC: A Typed Event-Focused Lexical Inference Benchmark for Evaluating Natural Language Inference**Martin Schmitt, Hinrich Schütze*2019 · 28 citations*](https://md.research.elamur.ai/free/en/papers/schmitt-2019-sherliic-typed-event-focused-lexical/)
339. **[Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance](https://md.research.elamur.ai/free/en/papers/nahum-2025-are-llms-better-than-reported/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Omer Nahum, Nitay Calderon, Orgad Keller, et al.*2025 · 27 citations*
340. **[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*
341. **[New Protocols and Negative Results for Textual Entailment Data Collection](https://md.research.elamur.ai/free/en/papers/bowman-2020-new-protocols-negative-results-textual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, et al.*2020 · 27 citations*
342. **[Sensitivity as a Complexity Measure for Sequence Classification Tasks](https://md.research.elamur.ai/free/en/papers/hahn-2021-sensitivity-as-complexity-measure-sequence/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Michael Hahn, Dan Jurafsky, Richard Futrell*2021 · 26 citations*
343. [**Understanding Retrieval Augmentation for Long-Form Question Answering arXiv:2310.12150**Hung-Ting Chen, Fangyuan Xu, Shane Arora, et al.*2023 · 24 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-understanding-retrieval-augmentation-long-form/)
344. **[Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation](https://md.research.elamur.ai/free/en/papers/truong-2022-not-another-negation-benchmark-nan/) [Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)](https://md.research.elamur.ai/free/en/venues/aacl/)**Hung Thinh Truong, Yulia Otmakhova, Timothy Baldwin, et al.*2022 · 24 citations*
345. [**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/)
346. [**ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding arXiv:2010.12148**Dongling Xiao, Yu-Kun Li, Han Zhang, et al.*2020 · 22 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2020-ernie-gram-pre-training/)
347. [**He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics**Nanjiang Jiang, Marie-Catherine de Marneffe*2021 · 21 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2021-he-thinks-he-knows-better/)
348. [**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/)
349. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
350. **[Ecologically Valid Explanations for Label Variation in NLI](https://md.research.elamur.ai/free/en/papers/jiang-2023-ecologically-valid-explanations-label-variation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nan-Jiang Jiang, Chenhao Tan, Marie-Catherine de Marneffe*2023 · 19 citations*
351. **[“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*
352. [**Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases arXiv:2306.12567**Risako Ando, Takanobu Morishita, Hirohiko Abe, et al.*2023 · 17 citations*](https://md.research.elamur.ai/free/en/papers/ando-2023-evaluating-large-language-models-neubaroco/)
353. **[Stress Test Evaluation of Transformer-based Models in Natural Language Understanding Tasks](https://md.research.elamur.ai/free/en/papers/aspillaga-2020-stress-test-evaluation-transformer-based/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Carlos Aspillaga, Andrés Carvallo, Vladimir Araujo*2020 · 17 citations*
354. **[FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluation](https://md.research.elamur.ai/free/en/papers/bayat-2025-factbench-dynamic-benchmark-wild-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Farima Fatahi Bayat, Lechen Zhang, Sheza Munir, et al.*2025 · 16 citations*
355. [**Better to Ask in English: Evaluation of Large Language Models on English, Low-resource and Cross-Lingual Settings arXiv:2410.13153**Krishno Dey, Prerona Tarannum, Md. Arid Hasan, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/dey-2024-better-ask-english-evaluation-large/)
356. **[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*
357. **[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*
358. **[Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?](https://md.research.elamur.ai/free/en/papers/razumovskaia-2025-analyzing-adapting-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Evgeniia Razumovskaia, Ivan Vulić, Anna Korhonen*2025 · 15 citations*
359. **[Neuron-Level Sequential Editing for Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2025-neuron-level-sequential-editing-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Houcheng Jiang, Junfeng Fang, Tianyu Zhang, et al.*2025 · 15 citations*
360. **[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*
361. [**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/)
362. **[Evaluating Large Language Models for Health-related Queries with Presuppositions](https://md.research.elamur.ai/free/en/papers/kaur-2024-evaluating-large-language-models-health/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Navreet Kaur, Monojit Choudhury, Danish Pruthi*2024 · 15 citations*
363. **[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*
364. **[Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference](https://md.research.elamur.ai/free/en/papers/wang-2022-capture-human-disagreement-distributions-calibrated/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxia Wang, Minghan Wang, Yimeng Chen, et al.*2022 · 15 citations*
365. **[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*
366. **[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*
367. **[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*
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369. **[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*
370. [**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/)
371. [**The ICL Consistency Test arXiv:2312.04945**Lucas Weber, Elia Bruni, Dieuwke Hupkes*2023 · 12 citations*](https://md.research.elamur.ai/free/en/papers/weber-2023-icl-consistency-test/)
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373. [**Combining Event Semantics and Degree Semantics for Natural Language Inference**Izumi Haruta, Koji Mineshima, Daisuke Bekki*2020 · 12 citations*](https://md.research.elamur.ai/free/en/papers/haruta-2020-combining-event-semantics-degree-semantics/)
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375. **[Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2025-unraveling-mechanics-learning-based-demonstration/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hui Liu, Wenya Wang, Hao Sun, et al.*2025 · 11 citations*
376. **[IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions](https://md.research.elamur.ai/free/en/papers/zeng-2023-iekg-commonsense-knowledge-graph-idiomatic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ziheng Zeng, Kellen Tan Cheng, Srihari Venkat Nanniyur, et al.*2023 · 11 citations*
377. [**Disentangling Indirect Answers to Yes-No Questions in Real Conversations**Krishna Sanagavarapu, Jathin Singaraju, Anusha Kakileti, et al.*2022 · 11 citations*](https://md.research.elamur.ai/free/en/papers/sanagavarapu-2022-disentangling-indirect-answers-yes-no/)
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386. [**ArNLI: Arabic Natural Language Inference for Entailment and Contradiction Detection arXiv:2209.13953 · (2023) Computer Science, 24(2)**Khloud Al Jallad, Nada Ghneim*2022 · 9 citations*](https://md.research.elamur.ai/free/en/papers/jallad-2022-arnli-arabic-natural-language-inference/)
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388. [**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/)
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395. [**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/)
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