---
title: "Who cites A large annotated — page 2 — 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…"
canonical: "https://research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/page/2/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/page/2/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [A large annotated corpus for learning natural…](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/) / [cited by](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/) / page 2

# 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 2 of 5 · 101–200 · [← 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/)

[← prev](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/) [1](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/) **2** [3](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/page/3/) [4](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/page/4/) [5](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/page/5/) [next →](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/page/3/)

101. **[Explainable Automated Fact-Checking for Public Health Claims](https://md.research.elamur.ai/free/en/papers/kotonya-2020-explainable-automated-fact-checking-public/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Neema Kotonya, Francesca Toni*2020 · 314 citations*
102. [**Abductive Commonsense Reasoning**Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, et al.*2019 · 309 citations*](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/)
103. **[Muppet: Massive Multi-task Representations with Pre-Finetuning](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-muppet-massive-multi-task-representations/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, et al.*2021 · 307 citations*
104. **[SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://md.research.elamur.ai/free/en/papers/vu-2022-spot-better-frozen-model-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tu Vu, Brian Lester, Noah Constant, et al.*2022 · 306 citations*
105. **[SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://md.research.elamur.ai/free/en/papers/jiang-2020-smart-robust-efficient-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoming Jiang, Pengcheng He, Weizhu Chen, et al.*2020 · 304 citations*
106. **[Learning to Write with Cooperative Discriminators](https://md.research.elamur.ai/free/en/papers/holtzman-2018-learning-write-cooperative-discriminators/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ari Holtzman, Jan Buys, Maxwell Forbes, et al.*2018 · 303 citations*
107. **[Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM](https://md.research.elamur.ai/free/en/papers/habernal-2016-which-argument-is-more-convincing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ivan Habernal, Iryna Gurevych*2016 · 303 citations*
108. **[Towards Debiasing Fact Verification Models](https://md.research.elamur.ai/free/en/papers/schuster-2019-debiasing-fact-verification-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, et al.*2019 · 301 citations*
109. **[Text Classification via Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2023-text-classification-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaofei Sun, Xiaoya Li, Jiwei Li, et al.*2023 · 292 citations*
110. **[Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://md.research.elamur.ai/free/en/papers/schuster-2021-get-your-vitamin-c-robust/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Tal Schuster, Adam Fisch, Regina Barzilay*2021 · 290 citations*
111. **[GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://md.research.elamur.ai/free/en/papers/zhou-2019-gear-graph-based-evidence-aggregating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jie Zhou, Xu Han, Cheng Yang, et al.*2019 · 289 citations*
112. [**ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning arXiv:2002.04326**Weihao Yu, Zihang Jiang, Yanfei Dong, et al.*2020 · 286 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-reclor-reading-comprehension-dataset-requiring/)
113. **[AlignScore: Evaluating Factual Consistency with A Unified Alignment Function](https://md.research.elamur.ai/free/en/papers/zha-2023-alignscore-evaluating-factual-consistency-unified/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuheng Zha, Yichi Yang, Ruichen Li, et al.*2023 · 284 citations*
114. **[TRUE: Re-evaluating Factual Consistency Evaluation](https://md.research.elamur.ai/free/en/papers/honovich-2022-true-re-evaluating-factual-consistency/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Or Honovich, Roee Aharoni, Jonathan Herzig, et al.*2022 · 279 citations*
115. **[Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models](https://md.research.elamur.ai/free/en/papers/wu-2021-polyjuice-generating-counterfactuals-explaining-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, et al.*2021 · 277 citations*
116. **[Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://md.research.elamur.ai/free/en/papers/thakur-2021-augmented-sbert-data-augmentation-method/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nandan Thakur, Nils Reimers, Johannes Daxenberger, et al.*2021 · 276 citations*
117. **[COVIDLies: Detecting COVID-19 Misinformation on Social Media](https://md.research.elamur.ai/free/en/papers/hossain-2020-covidlies-detecting-covid-19-misinformation/) [Workshop on NLP for COVID-19 at ACL](https://md.research.elamur.ai/free/en/venues/nlpcovid19/)**Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, et al.*2020 · 271 citations*
118. **[An Analysis of Simple Data Augmentation for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/dai-2020-analysis-simple-data-augmentation-named/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Xiang Dai, Heike Adel*2020 · 266 citations*
119. **[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 · 263 citations*
120. **[Large Language Models for Data Annotation and Synthesis: A Survey](https://md.research.elamur.ai/free/en/papers/tan-2024-large-language-models-data-annotation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Tan, Dawei Li, Song Wang, et al.*2024 · 262 citations*
121. **[UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification](https://md.research.elamur.ai/free/en/papers/hanselowski-2018-ukp-athene-multi-sentence-textual/) [Fact Extraction and VERification Workshop (FEVER)](https://md.research.elamur.ai/free/en/venues/fever/)**Andreas Hanselowski, Hao Zhang, Zile Li, et al.*2018 · 261 citations*
122. **[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 · 260 citations*
123. **[DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 259 citations*
124. **[The Multilingual Amazon Reviews Corpus](https://md.research.elamur.ai/free/en/papers/keung-2020-multilingual-amazon-reviews-corpus/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Phillip Keung, Yichao Lu, György Szarvas, et al.*2020 · 257 citations*
125. **[Multi-Reward Reinforced Summarization with Saliency and Entailment](https://md.research.elamur.ai/free/en/papers/pasunuru-2018-multi-reward-reinforced-summarization-saliency/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ramakanth Pasunuru, Mohit Bansal*2018 · 255 citations*
126. **[Generating Datasets with Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Timo Schick, Hinrich Schutze*2021 · 254 citations*
127. **[GPT-RE: In-context Learning for Relation Extraction using Large Language Models](https://md.research.elamur.ai/free/en/papers/wan-2023-gpt-re-context-learning-relation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Wan, Fei Cheng, Zhuoyuan Mao, et al.*2023 · 253 citations*
128. [**Learning and Evaluating General Linguistic Intelligence arXiv:1901.11373**Dani Yogatama, Cyprien de Masson d'Autume, Jerome Connor, et al.*2019 · 248 citations*](https://md.research.elamur.ai/free/en/papers/yogatama-2019-learning-evaluating-general-linguistic-intelligence/)
129. **[Charagram: Embedding Words and Sentences via Character n-grams](https://md.research.elamur.ai/free/en/papers/wieting-2016-charagram-embedding-words-sentences-character/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**John Wieting, Mohit Bansal, Kevin Gimpel, et al.*2016 · 242 citations*
130. **[Evaluating Commonsense in Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2019-evaluating-commonsense-pre-trained-language/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Xuhui Zhou, Yue Zhang, Leyang Cui, et al.*2019 · 235 citations*
131. **[Self-Guided Contrastive Learning for BERT Sentence Representations](https://md.research.elamur.ai/free/en/papers/kim-2021-self-guided-contrastive-learning-bert/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Taeuk Kim, Kang Min Yoo, Sang-goo Lee*2021 · 234 citations*
132. **[HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification](https://md.research.elamur.ai/free/en/papers/jiang-2020-hover-dataset-many-hop-fact/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yichen Jiang, Shikha Bordia, Zheng Zhong, et al.*2020 · 228 citations*
133. **[The Importance of Being Recurrent for Modeling Hierarchical Structure](https://md.research.elamur.ai/free/en/papers/tran-2018-importance-being-recurrent-modeling-hierarchical/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ke Tran, Arianna Bisazza, Christof Monz*2018 · 228 citations*
134. **[Measuring Association Between Labels and Free-Text Rationales](https://md.research.elamur.ai/free/en/papers/wiegreffe-2021-measuring-association-between-labels-free/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sarah Wiegreffe, Ana Marasović, Noah A. Smith*2021 · 220 citations*
135. [**Transforming Question Answering Datasets Into Natural Language Inference Datasets arXiv:1809.02922**Dorottya Demszky, Kelvin Guu, Percy Liang*2018 · 220 citations*](https://md.research.elamur.ai/free/en/papers/demszky-2018-transforming-question-answering-datasets-natural/)
136. [**FOLIO: Natural Language Reasoning with First-Order Logic arXiv:2209.00840**Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/han-2022-folio-natural-language-reasoning-first/)
137. [**Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4 arXiv:2304.03439**Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-logical-reasoning-ability-chatgpt/)
138. [**Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch arXiv:2311.03099**Le Yu, Bowen Yu, Haiyang Yu, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-language-models-super-mario-absorbing/)
139. **[Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training](https://md.research.elamur.ai/free/en/papers/li-2020-don-t-say-that-making/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Margaret Li, Stephen Roller, Ilia Kulikov, et al.*2020 · 217 citations*
140. **[The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants](https://md.research.elamur.ai/free/en/papers/habernal-2018-argument-reasoning-comprehension-task-identification/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, et al.*2018 · 217 citations*
141. **[An Unsupervised Sentence Embedding Method by Mutual Information Maximization](https://md.research.elamur.ai/free/en/papers/zhang-2020-unsupervised-sentence-embedding-method-mutual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yan Zhang, Ruidan He, Zuozhu Liu, et al.*2020 · 213 citations*
142. **[Data-Driven Sentence Simplification: Survey and Benchmark](https://md.research.elamur.ai/free/en/papers/manchego-2020-data-driven-sentence-simplification-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Fernando Alva-Manchego, Carolina Scarton, Lucia Specia*2020 · 213 citations*
143. **[Sources of Hallucination by Large Language Models on Inference Tasks](https://md.research.elamur.ai/free/en/papers/mckenna-2023-sources-hallucination-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nick McKenna, Tianyi Li, Liang Cheng, et al.*2023 · 212 citations*
144. **[“Kelly is a Warm Person, Joseph is a Role Model”: Gender Biases in LLM-Generated Reference Letters](https://md.research.elamur.ai/free/en/papers/wan-2023-kelly-is-warm-person-joseph/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yixin Wan, George Pu, Jiao Sun, et al.*2023 · 211 citations*
145. **[Explaining Answers with Entailment Trees](https://md.research.elamur.ai/free/en/papers/dalvi-2021-explaining-answers-entailment-trees/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, et al.*2021 · 211 citations*
146. [**From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline arXiv:2406.11939**Tianle Li, Wei-Lin Chiang, Evan Frick, et al.*2024 · 210 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-crowdsourced-data-high-quality-benchmarks/)
147. **[Logical Natural Language Generation from Open-Domain Tables](https://md.research.elamur.ai/free/en/papers/chen-2020-logical-natural-language-generation-open/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenhu Chen, Jianshu Chen, Yu Su, et al.*2020 · 210 citations*
148. **[Merging Models with Fisher-Weighted Averaging](https://md.research.elamur.ai/free/en/papers/matena-2021-merging-models-fisher-weighted-averaging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Matena, Colin Raffel*2021 · 208 citations*
149. **[Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space](https://md.research.elamur.ai/free/en/papers/li-2020-optimus-organizing-sentences-pre-trained/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chunyuan Li, Xiang Gao, Yuan Li, et al.*2020 · 208 citations*
150. **[Evaluating Factuality in Generation with Dependency-level Entailment](https://md.research.elamur.ai/free/en/papers/goyal-2020-evaluating-factuality-generation-dependency-level/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tanya Goyal, Greg Durrett*2020 · 205 citations*
151. **[Exploring and Predicting Transferability across NLP Tasks](https://md.research.elamur.ai/free/en/papers/vu-2020-exploring-predicting-transferability-across-nlp/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tu Vu, Tong Wang, Tsendsuren Munkhdalai, et al.*2020 · 198 citations*
152. **[Generative Data Augmentation for Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/yang-2020-generative-data-augmentation-commonsense-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiben Yang, Chaitanya Malaviya, Jared Fernandez, et al.*2020 · 198 citations*
153. **[Towards Faithful Model Explanation in NLP: A Survey](https://md.research.elamur.ai/free/en/papers/lyu-2024-faithful-model-explanation-nlp-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Qing Lyu, Marianna Apidianaki, Chris Callison-Burch*2024 · 197 citations*
154. **[Q²: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering](https://md.research.elamur.ai/free/en/papers/honovich-2021-q-2-evaluating-factual-consistency/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Or Honovich, Leshem Choshen, Roee Aharoni, et al.*2021 · 196 citations*
155. **[The Right Tool for the Job: Matching Model and Instance Complexities](https://md.research.elamur.ai/free/en/papers/schwartz-2020-right-tool-job-matching-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, et al.*2020 · 196 citations*
156. **[What Can We Learn from Collective Human Opinions on Natural Language Inference Data?](https://md.research.elamur.ai/free/en/papers/nie-2020-what-can-we-learn-collective/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yixin Nie, Xiang Zhou, Mohit Bansal*2020 · 196 citations*
157. [**Making Retrieval-Augmented Language Models Robust to Irrelevant Context arXiv:2310.01558**Ori Yoran, Tomer Wolfson, Ori Ram, et al.*2023 · 195 citations*](https://md.research.elamur.ai/free/en/papers/yoran-2023-making-retrieval-augmented-language-models/)
158. **[Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation](https://md.research.elamur.ai/free/en/papers/poliak-2018-collecting-diverse-natural-language-inference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Adam Poliak, Aparajita Haldar, Rachel Rudinger, et al.*2018 · 193 citations*
159. **[Challenges in Automated Debiasing for Toxic Language Detection](https://md.research.elamur.ai/free/en/papers/zhou-2021-challenges-automated-debiasing-toxic-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, et al.*2021 · 191 citations*
160. **[Revealing the Importance of Semantic Retrieval for Machine Reading at Scale](https://md.research.elamur.ai/free/en/papers/nie-2019-revealing-importance-semantic-retrieval-machine/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yixin Nie, Songhe Wang, Mohit Bansal*2019 · 190 citations*
161. [**SGPT: GPT Sentence Embeddings for Semantic Search arXiv:2202.08904**Niklas Muennighoff*2022 · 189 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-sgpt-gpt-sentence-embeddings-semantic/)
162. **[A Survey on Stance Detection for Mis- and Disinformation Identification](https://md.research.elamur.ai/free/en/papers/hardalov-2022-survey-stance-detection-mis-disinformation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Momchil Hardalov, Arnav Arora, Preslav Nakov, et al.*2022 · 188 citations*
163. **[NILE : Natural Language Inference with Faithful Natural Language Explanations](https://md.research.elamur.ai/free/en/papers/kumar-2020-nile-natural-language-inference-faithful/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sawan Kumar, Partha Talukdar*2020 · 184 citations*
164. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
165. [**Learning To Use Formulas To Solve Simple Arithmetic Problems**Arindam Mitra, Chitta Baral*2016 · 178 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2016-learning-use-formulas-solve-simple/)
166. [**Order-Embeddings of Images and Language arXiv:1511.06361**Ivan Vendrov, Ryan Kiros, Sanja Fidler, et al.*2015 · 177 citations*](https://md.research.elamur.ai/free/en/papers/vendrov-2015-order-embeddings-images-language/)
167. **[Translation Artifacts in Cross-lingual Transfer Learning](https://md.research.elamur.ai/free/en/papers/artetxe-2020-translation-artifacts-cross-lingual-transfer/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mikel Artetxe, Gorka Labaka, Eneko Agirre*2020 · 176 citations*
168. **[Reasoning Over Paragraph Effects in Situations](https://md.research.elamur.ai/free/en/papers/lin-2019-reasoning-over-paragraph-effects-situations/) [Workshop on Machine Reading for Question Answering](https://md.research.elamur.ai/free/en/venues/mrqa/)**Kevin Lin, Oyvind Tafjord, Peter Clark, et al.*2019 · 176 citations*
169. [**StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding arXiv:1908.04577**Wei Wang, Bin Bi, Ming Yan, et al.*2019 · 175 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-structbert-incorporating-language-structures-pre/)
170. [**PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts arXiv:2202.01279**Stephen H. Bach, Victor Sanh, Zheng-Xin Yong, et al.*2022 · 174 citations*](https://md.research.elamur.ai/free/en/papers/bach-2022-promptsource-integrated-development-environment-repository/)
171. **[Social Bias in Elicited Natural Language Inferences](https://md.research.elamur.ai/free/en/papers/rudinger-2017-social-bias-elicited-natural-language/) [ACL Workshop on Ethics in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/acl-workshop-on-ethics-in-natural-language-processing/)**Rachel Rudinger, Chandler May, Benjamin Van Durme*2017 · 170 citations*
172. **[Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction](https://md.research.elamur.ai/free/en/papers/sainz-2021-label-verbalization-entailment-effective-zero/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, et al.*2021 · 167 citations*
173. **[MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liyan Tang, Philippe Laban, Greg Durrett*2024 · 166 citations*
174. **[AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages](https://md.research.elamur.ai/free/en/papers/ebrahimi-2022-americasnli-evaluating-zero-shot-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Abteen Ebrahimi, Manuel Mager, Arturo Oncevay, et al.*2022 · 164 citations*
175. [**Language models show human-like content effects on reasoning tasks arXiv:2207.07051**Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/dasgupta-2022-language-models-show-human-like/)
176. [**Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition**Paloma Jeretic, Alex Warstadt, Suvrat Bhooshan, et al.*2020 · 161 citations*](https://md.research.elamur.ai/free/en/papers/jeretic-2020-are-natural-language-inference-models/)
177. **[Inferring Which Medical Treatments Work from Reports of Clinical Trials](https://md.research.elamur.ai/free/en/papers/lehman-2019-inferring-which-medical-treatments-work/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Eric Lehman, Jay DeYoung, Regina Barzilay, et al.*2019 · 160 citations*
178. **[Specialising Word Vectors for Lexical Entailment](https://md.research.elamur.ai/free/en/papers/vulic-2018-specialising-word-vectors-lexical-entailment/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ivan Vulić, Nikola Mrkšić*2018 · 160 citations*
179. **[Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering](https://md.research.elamur.ai/free/en/papers/wu-2022-self-adaptive-context-learning-information/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, et al.*2022 · 159 citations*
180. **[Understanding tables with intermediate pre-training](https://md.research.elamur.ai/free/en/papers/eisenschlos-2020-understanding-tables-intermediate-pre-training/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Julian Eisenschlos, Syrine Krichene, Thomas Müller*2020 · 158 citations*
181. **[Reducing Quantity Hallucinations in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/zhao-2020-reducing-quantity-hallucinations-abstractive-summarization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zheng Zhao, Shay B. Cohen, Bonnie Webber*2020 · 157 citations*
182. [**ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning arXiv:2212.07919**Olga Golovneva, Moya Chen, Spencer Poff, et al.*2022 · 155 citations*](https://md.research.elamur.ai/free/en/papers/golovneva-2022-roscoe-suite-metrics-scoring-step/)
183. **[Fine-Tuning Language Models with Just Forward Passes](https://md.research.elamur.ai/free/en/papers/malladi-2023-fine-tuning-language-models-just/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sadhika Malladi, Tianyu Gao, Eshaan Nichani, et al.*2023 · 153 citations*
184. [**Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation arXiv:2305.15852**Niels Mundler, Jingxuan He, Slobodan Jenko, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/mundler-2023-self-contradictory-hallucinations-large-language/)
185. **[Reevaluating Adversarial Examples in Natural Language](https://md.research.elamur.ai/free/en/papers/morris-2020-reevaluating-adversarial-examples-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**John Morris, Eli Lifland, Jack Lanchantin, et al.*2020 · 151 citations*
186. [**A Review on Language Models as Knowledge Bases arXiv:2204.06031**Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, et al.*2022 · 150 citations*](https://md.research.elamur.ai/free/en/papers/alkhamissi-2022-review-language-models-knowledge-bases/)
187. **[INFOTABS: Inference on Tables as Semi-structured Data](https://md.research.elamur.ai/free/en/papers/gupta-2020-infotabs-inference-tables-as-semi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vivek Gupta, Maitrey Mehta, Pegah Nokhiz, et al.*2020 · 150 citations*
188. **[BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data](https://md.research.elamur.ai/free/en/papers/song-2021-bob-bert-over-bert-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoyu Song, Yan Wang, Kaiyan Zhang, et al.*2021 · 149 citations*
189. **[Do latent tree learning models identify meaningful structure in sentences?](https://md.research.elamur.ai/free/en/papers/williams-2018-do-latent-tree-learning-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Adina Williams, Andrew Drozdov, Samuel R. Bowman*2018 · 149 citations*
190. [**OCNLI: Original Chinese Natural Language Inference arXiv:2010.05444**Hai Hu, Kyle Richardson, Liang Xu, et al.*2020 · 148 citations*](https://md.research.elamur.ai/free/en/papers/hu-2020-ocnli-original-chinese-natural/)
191. **[HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment](https://md.research.elamur.ai/free/en/papers/vulic-2017-hyperlex-large-scale-evaluation-graded/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Ivan Vulić, Daniela Gerz, Douwe Kiela, et al.*2017 · 148 citations*
192. **[Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders](https://md.research.elamur.ai/free/en/papers/liu-2021-fast-effective-self-supervised-transforming/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fangyu Liu, Ivan Vulić, Anna Korhonen, et al.*2021 · 147 citations*
193. **[A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking](https://md.research.elamur.ai/free/en/papers/hanselowski-2019-richly-annotated-corpus-different-tasks/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Andreas Hanselowski, Christian Stab, Claudia Schulz, et al.*2019 · 147 citations*
194. **[Unified Demonstration Retriever for In-Context Learning](https://md.research.elamur.ai/free/en/papers/li-2023-unified-demonstration-retriever-context-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaonan Li, Kai Lv, Hang Yan, et al.*2023 · 146 citations*
195. **[Efficient Methods for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/treviso-2023-efficient-methods-natural-language-processing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marcos Treviso, Ji-Ung Lee, Tianchu Ji, et al.*2023 · 145 citations*
196. **[From Arguments to Key Points: Towards Automatic Argument Summarization](https://md.research.elamur.ai/free/en/papers/haim-2020-arguments-key-points-automatic-argument/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Roy Bar-Haim, Lilach Eden, Roni Friedman, et al.*2020 · 144 citations*
197. **[Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://md.research.elamur.ai/free/en/papers/bartolo-2020-beat-ai-investigating-adversarial-human/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Max Bartolo, Alastair Roberts, Johannes Welbl, et al.*2020 · 141 citations*
198. **[RUSE: Regressor Using Sentence Embeddings for Automatic Machine Translation Evaluation](https://md.research.elamur.ai/free/en/papers/shimanaka-2018-ruse-regressor-sentence-embeddings-automatic/) [Conference on Machine Translation: Shared Task Papers](https://md.research.elamur.ai/free/en/venues/conference-on-machine-translation-shared-task-papers/)**Hiroki Shimanaka, Tomoyuki Kajiwara, Mamoru Komachi*2018 · 141 citations*
199. [**Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models arXiv:2212.08037**Bernd Bohnet, Vinh Q. Tran, Pat Verga, et al.*2022 · 138 citations*](https://md.research.elamur.ai/free/en/papers/bohnet-2022-attributed-question-answering-evaluation-modeling/)
200. **[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/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nelson F. Liu, Roy Schwartz, Noah A. Smith*2019 · 138 citations*

[← prev](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/) [1](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/) **2** [3](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/page/3/) [4](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/page/4/) [5](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/page/5/) [next →](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/cited-by/page/3/)

- [← back to](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/) — A large annotated corpus for learning natural language inference