---
title: "Who cites A Broad-Coverage — page 2 — 2,320 citing papers"
description: "2,320 papers of the Elamur Research library cite «A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference»…"
canonical: "https://research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/page/2/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/page/2/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [A Broad-Coverage Challenge Corpus for Sentence…](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/) / [cited by](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/) / page 2

# 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) · 448 with a page here, the most cited first · page 2 of 5 · 101–200 · [← 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/)

[← prev](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/) [1](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/) **2** [3](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/page/3/) [4](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/page/4/) [5](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/page/5/) [next →](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/page/3/)

101. **[Byte Pair Encoding is Suboptimal for Language Model Pretraining](https://md.research.elamur.ai/free/en/papers/bostrom-2020-byte-pair-encoding-is-suboptimal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaj Bostrom, Greg Durrett*2020 · 310 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. **[UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning](https://md.research.elamur.ai/free/en/papers/li-2021-unimo-unified-modal-understanding-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wei Li, Can Gao, Guocheng Niu, et al.*2021 · 308 citations*
104. **[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*
105. **[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*
106. [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts arXiv:2306.04528**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/)
107. **[ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://md.research.elamur.ai/free/en/papers/yao-2022-zeroquant-efficient-affordable-post-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al.*2022 · 304 citations*
108. **[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*
109. **[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*
110. [**FlauBERT: Unsupervised Language Model Pre-training for French arXiv:1912.05372**Hang Le, Loïc Vial, Jibril Frej, et al.*2019 · 299 citations*](https://md.research.elamur.ai/free/en/papers/le-2019-flaubert-unsupervised-language-model-pre/)
111. **[Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al.*2021 · 297 citations*
112. **[BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla](https://md.research.elamur.ai/free/en/papers/bhattacharjee-2022-banglabert-language-model-pretraining-benchmarks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, et al.*2022 · 296 citations*
113. **[QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization](https://md.research.elamur.ai/free/en/papers/fabbri-2022-qafacteval-improved-qa-based-factual/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, et al.*2022 · 292 citations*
114. **[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*
115. **[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*
116. [**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/)
117. **[BAM! Born-Again Multi-Task Networks for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/clark-2019-bam-born-again-multi-task/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, et al.*2019 · 285 citations*
118. **[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*
119. **[Contextualized Perturbation for Textual Adversarial Attack](https://md.research.elamur.ai/free/en/papers/li-2021-contextualized-perturbation-textual-adversarial-attack/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Dianqi Li, Yizhe Zhang, Hao Peng, et al.*2021 · 283 citations*
120. **[XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation](https://md.research.elamur.ai/free/en/papers/ruder-2021-xtreme-r-more-challenging-nuanced/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sebastian Ruder, Noah Constant, Jan Botha, et al.*2021 · 281 citations*
121. **[Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?](https://md.research.elamur.ai/free/en/papers/pruksachatkun-2020-intermediate-task-transfer-learning-pretrained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yada Pruksachatkun, Jason Phang, Haokun Liu, et al.*2020 · 281 citations*
122. **[OpenPrompt: An Open-source Framework for Prompt-learning](https://md.research.elamur.ai/free/en/papers/ding-2022-openprompt-open-source-framework-prompt/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ning Ding, Shengding Hu, Weilin Zhao, et al.*2022 · 279 citations*
123. **[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*
124. **[Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks](https://md.research.elamur.ai/free/en/papers/huang-2019-unicoder-universal-language-encoder-pre/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Haoyang Huang, Yaobo Liang, Nan Duan, et al.*2019 · 265 citations*
125. **[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*
126. **[The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://md.research.elamur.ai/free/en/papers/bandarkar-2023-belebele-benchmark-parallel-reading-comprehension/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lucas Bandarkar, Davis Liang, Benjamin Muller, et al.*2023 · 262 citations*
127. **[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*
128. **[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*
129. **[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*
130. [**CLUE: A Chinese Language Understanding Evaluation Benchmark arXiv:2004.05986**Liang Xu, Hai Hu, Xuanwei Zhang, et al.*2020 · 244 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-clue-chinese-language-understanding/)
131. **[BERT-of-Theseus: Compressing BERT by Progressive Module Replacing](https://md.research.elamur.ai/free/en/papers/xu-2020-bert-theseus-compressing-bert-progressive/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Canwen Xu, Wangchunshu Zhou, Tao Ge, et al.*2020 · 240 citations*
132. **[Curriculum Learning for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/xu-2020-curriculum-learning-natural-language-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Benfeng Xu, Licheng Zhang, Zhendong Mao, et al.*2020 · 240 citations*
133. **[mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://md.research.elamur.ai/free/en/papers/zhang-2024-mgte-generalized-long-context-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xin Zhang, Yanzhao Zhang, Dingkun Long, et al.*2024 · 237 citations*
134. **[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*
135. **[Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification](https://md.research.elamur.ai/free/en/papers/schick-2020-automatically-identifying-words-that-can/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Timo Schick, Helmut Schmid, Hinrich Schütze*2020 · 234 citations*
136. **[Coreferential Reasoning Learning for Language Representation](https://md.research.elamur.ai/free/en/papers/ye-2020-coreferential-reasoning-learning-language-representation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Deming Ye, Yankai Lin, Jiaju Du, et al.*2020 · 228 citations*
137. **[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*
138. **[Visualizing and Understanding the Effectiveness of BERT](https://md.research.elamur.ai/free/en/papers/hao-2019-visualizing-understanding-effectiveness-bert/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yaru Hao, Li Dong, Furu Wei, et al.*2019 · 222 citations*
139. **[Structured Pruning Learns Compact and Accurate Models](https://md.research.elamur.ai/free/en/papers/xia-2022-structured-pruning-learns-compact-accurate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhou Xia, Zexuan Zhong, Danqi Chen*2022 · 221 citations*
140. **[Syntactic Data Augmentation Increases Robustness to Inference Heuristics](https://md.research.elamur.ai/free/en/papers/min-2020-syntactic-data-augmentation-increases-robustness/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junghyun Min, R. Thomas McCoy, Dipanjan Das, et al.*2020 · 220 citations*
141. **[What Happens To BERT Embeddings During Fine-tuning?](https://md.research.elamur.ai/free/en/papers/merchant-2020-what-happens-bert-embeddings-during/) [BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/blackboxnlp/)**Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, et al.*2020 · 220 citations*
142. **[Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations](https://md.research.elamur.ai/free/en/papers/jung-2022-maieutic-prompting-logically-consistent-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jaehun Jung, Lianhui Qin, Sean Welleck, et al.*2022 · 219 citations*
143. [**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/)
144. [**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/)
145. **[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*
146. **[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*
147. **[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*
148. [**Generating Training Data with Language Models: Towards Zero-Shot Language Understanding arXiv:2202.04538**Yu Meng, Jiaxin Huang, Yu Zhang, et al.*2022 · 210 citations*](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/)
149. **[“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 · 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. **[Block Pruning For Faster Transformers](https://md.research.elamur.ai/free/en/papers/lagunas-2021-block-pruning-faster-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**François Lagunas, Ella Charlaix, Victor Sanh, et al.*2021 · 199 citations*
152. **[Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting](https://md.research.elamur.ai/free/en/papers/hu-2019-improved-lexically-constrained-decoding-translation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**J. Edward Hu, Huda Khayrallah, Ryan Culkin, et al.*2019 · 199 citations*
153. [**COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining arXiv:2102.08473**Yu Meng, Chenyan Xiong, Payal Bajaj, et al.*2021 · 198 citations*](https://md.research.elamur.ai/free/en/papers/meng-2021-coco-lm-correcting-contrasting-text/)
154. **[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*
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. [**One Embedder, Any Task: Instruction-Finetuned Text Embeddings arXiv:2212.09741**Hongjin Su, Weijia Shi, Jungo Kasai, et al.*2022 · 194 citations*](https://md.research.elamur.ai/free/en/papers/su-2022-one-embedder-any-task-instruction/)
159. **[That is a Known Lie: Detecting Previously Fact-Checked Claims](https://md.research.elamur.ai/free/en/papers/shaar-2020-that-is-known-lie-detecting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shaden Shaar, Nikolay Babulkov, Giovanni Da San Martino, et al.*2020 · 193 citations*
160. **[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*
161. **[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*
162. **[Enhancing Factual Consistency of Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/zhu-2021-enhancing-factual-consistency-abstractive-summarization/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chenguang Zhu, William Hinthorn, Ruochen Xu, et al.*2021 · 190 citations*
163. [**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/)
164. **[Gradient-based Adversarial Attacks against Text Transformers](https://md.research.elamur.ai/free/en/papers/guo-2021-gradient-based-adversarial-attacks-against/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, et al.*2021 · 189 citations*
165. **[Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions](https://md.research.elamur.ai/free/en/papers/han-2020-explaining-black-box-predictions-unveiling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaochuang Han, Byron C. Wallace, Yulia Tsvetkov*2020 · 187 citations*
166. [**Charformer: Fast Character Transformers via Gradient-based Subword Tokenization arXiv:2106.12672**Yi Tay, Vinh Q. Tran, Sebastian Ruder, et al.*2021 · 186 citations*](https://md.research.elamur.ai/free/en/papers/tay-2021-charformer-fast-character-transformers-gradient/)
167. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations*
168. [**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/)
169. **[Planning with Learned Entity Prompts for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/narayan-2021-planning-learned-entity-prompts-abstractive/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Shashi Narayan, Yao Zhao, Joshua Maynez, et al.*2021 · 180 citations*
170. **[Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs](https://md.research.elamur.ai/free/en/papers/warstadt-2019-investigating-bert-s-knowledge-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alex Warstadt, Yu Cao, Ioana Grosu, et al.*2019 · 179 citations*
171. **[Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP](https://md.research.elamur.ai/free/en/papers/goot-2021-massive-choice-ample-tasks-machamp/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Rob van der Goot, Ahmet Üstün, Alan Ramponi, et al.*2021 · 177 citations*
172. **[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*
173. **[Debiasing Pre-trained Contextualised Embeddings](https://md.research.elamur.ai/free/en/papers/kaneko-2021-debiasing-pre-trained-contextualised-embeddings/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Masahiro Kaneko, Danushka Bollegala*2021 · 175 citations*
174. **[Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages](https://md.research.elamur.ai/free/en/papers/doddapaneni-2023-leaving-no-indic-language-behind/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, et al.*2023 · 174 citations*
175. **[Lifting the Curse of Multilinguality by Pre-training Modular Transformers](https://md.research.elamur.ai/free/en/papers/pfeiffer-2022-lifting-curse-multilinguality-pre-training/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jonas Pfeiffer, Naman Goyal, Xi Victoria Lin, et al.*2022 · 174 citations*
176. **[Self-training Improves Pre-training for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/du-2021-self-training-improves-pre-training/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jingfei Du, Edouard Grave, Beliz Gunel, et al.*2021 · 172 citations*
177. **[Zero-Shot Cross-Lingual Transfer with Meta Learning](https://md.research.elamur.ai/free/en/papers/nooralahzadeh-2020-zero-shot-cross-lingual-transfer/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva, et al.*2020 · 171 citations*
178. **[COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic](https://md.research.elamur.ai/free/en/papers/saakyan-2021-covid-fact-fact-extraction-verification/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Arkadiy Saakyan, Tuhin Chakrabarty, Smaranda Muresan*2021 · 169 citations*
179. **[QuALITY: Question Answering with Long Input Texts, Yes!](https://md.research.elamur.ai/free/en/papers/pang-2022-quality-question-answering-long-input/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, et al.*2022 · 167 citations*
180. **[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*
181. **[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*
182. **[Sustainable Modular Debiasing of Language Models](https://md.research.elamur.ai/free/en/papers/lauscher-2021-sustainable-modular-debiasing-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Anne Lauscher, Tobias Lueken, Goran Glavaš*2021 · 165 citations*
183. **[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*
184. **[Probing What Different NLP Tasks Teach Machines about Function Word Comprehension](https://md.research.elamur.ai/free/en/papers/kim-2019-probing-what-different-nlp-tasks/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Najoung Kim, Roma Patel, Adam Poliak, et al.*2019 · 163 citations*
185. [**ChatGPT as a Factual Inconsistency Evaluator for Text Summarization arXiv:2303.15621**Zheheng Luo, Qianqian Xie, Sophia Ananiadou*2023 · 161 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-chatgpt-factual-inconsistency-evaluator-text/)
186. **[Larger-Scale Transformers for Multilingual Masked Language Modeling](https://md.research.elamur.ai/free/en/papers/goyal-2021-larger-scale-transformers-multilingual-masked/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Naman Goyal, Jingfei Du, Myle Ott, et al.*2021 · 161 citations*
187. [**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/)
188. **[Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance](https://md.research.elamur.ai/free/en/papers/utama-2020-mind-trade-off-debiasing-nlu/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Prasetya Ajie Utama, Nafise Sadat Moosavi, Iryna Gurevych*2020 · 159 citations*
189. **[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*
190. **[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*
191. **[Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks](https://md.research.elamur.ai/free/en/papers/dou-2019-investigating-meta-learning-algorithms-low/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zi-Yi Dou, Keyi Yu, Antonios Anastasopoulos*2019 · 150 citations*
192. **[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*
193. **[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*
194. **[Language Model Behavior: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/chang-2024-language-model-behavior-comprehensive-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Tyler A. Chang, Benjamin K. Bergen*2024 · 148 citations*
195. **[Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation](https://md.research.elamur.ai/free/en/papers/mosbach-2023-few-shot-fine-tuning-vs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Marius Mosbach, Tiago Pimentel, Shauli Ravfogel, et al.*2023 · 148 citations*
196. [**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/)
197. **[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*
198. **[Fact-Checking Complex Claims with Program-Guided Reasoning](https://md.research.elamur.ai/free/en/papers/pan-2023-fact-checking-complex-claims-program/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liangming Pan, Xiaobao Wu, Xinyuan Lu, et al.*2023 · 146 citations*
199. **[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*
200. **[Probing the Probing Paradigm: Does Probing Accuracy Entail Task Relevance?](https://md.research.elamur.ai/free/en/papers/ravichander-2021-probing-probing-paradigm-does-probing/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Abhilasha Ravichander, Yonatan Belinkov, Eduard Hovy*2021 · 144 citations*

[← prev](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/) [1](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/) **2** [3](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/page/3/) [4](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/page/4/) [5](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/page/5/) [next →](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/cited-by/page/3/)

- [← back to](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/) — A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference