---
title: "Who cites Jian Zhang — 3,396 papers of our library"
description: "3,396 papers of the Elamur Research library cite the works of Jian Zhang, found in their bibliographies · page 1 of 5 · 1–100; the most cited first."
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---
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# Who cites Jian Zhang

3,401 citations of their works from 3,396 papers of our library (of the 141,840 whose bibliographies we read) · 447 with a page here, the most cited first · page 1 of 5 · 1–100 · [← Jian Zhang](https://md.research.elamur.ai/free/en/authors/jian-zhang/)

**1** [2](https://md.research.elamur.ai/free/en/authors/jian-zhang/cited-by/page/2/) … [5](https://md.research.elamur.ai/free/en/authors/jian-zhang/cited-by/page/5/) [next →](https://md.research.elamur.ai/free/en/authors/jian-zhang/cited-by/page/2/)

1. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,088 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/) **[BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://md.research.elamur.ai/free/en/papers/lee-2019-biobert-pre-trained-biomedical-language/) [Bioinformatics](https://md.research.elamur.ai/free/en/venues/bioinformatics/)**Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, et al.*2019 · 8,311 citations* **[GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/) [EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/emnlp-workshop-blackboxnlp-analyzing-and-interpreting-neural-networks-for-nlp/)**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,169 citations* **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,396 citations* **[Dense Passage Retrieval for Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vladimir Karpukhin, Barlas Oguz, Sewon Min, et al.*2020 · 5,375 citations* **[Natural Questions: A Benchmark for Question Answering Research](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, et al.*2019 · 4,035 citations* [**XLNet: Generalized Autoregressive Pretraining for Language Understanding arXiv:1906.08237**Zhilin Yang, Zihang Dai, Yiming Yang, et al.*2019 · 3,789 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/) **[HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhilin Yang, Peng Qi, Saizheng Zhang, et al.*2018 · 3,495 citations* **[Know What You Don't Know: Unanswerable Questions for SQuAD](https://md.research.elamur.ai/free/en/papers/rajpurkar-2018-know-what-you-don-t/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pranav Rajpurkar, Robin Jia, Percy Liang*2018 · 3,373 citations* **[TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mandar Joshi, Eunsol Choi, Daniel Weld, et al.*2017 · 3,213 citations* **[mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer](https://md.research.elamur.ai/free/en/papers/xue-2021-mt5-massively-multilingual-pre-trained/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Linting Xue, Noah Constant, Adam Roberts, et al.*2021 · 3,197 citations* [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,056 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) **[Language Models as Knowledge Bases?](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, et al.*2019 · 2,992 citations* [**RoFormer: Enhanced Transformer with Rotary Position Embedding arXiv:2104.09864**Jianlin Su, Yu Lu, Shengfeng Pan, et al.*2021 · 2,963 citations*](https://md.research.elamur.ai/free/en/papers/su-2021-roformer-enhanced-transformer-rotary-position/) **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,863 citations* **[LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://md.research.elamur.ai/free/en/papers/tan-2019-lxmert-learning-cross-modality-encoder/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hao Tan, Mohit Bansal*2019 · 2,730 citations* **[SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,598 citations* **[Reading Wikipedia to Answer Open-Domain Questions](https://md.research.elamur.ai/free/en/papers/chen-2017-reading-wikipedia-answer-open-domain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Danqi Chen, Adam Fisch, Jason Weston, et al.*2017 · 2,260 citations* **[FEVER: a Large-scale Dataset for Fact Extraction and VERification](https://md.research.elamur.ai/free/en/papers/thorne-2018-fever-large-scale-dataset-fact/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**James Thorne, Andreas Vlachos, Christos Christodoulopoulos, et al.*2018 · 2,207 citations* [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems arXiv:1905.00537**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,190 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/) [**Bidirectional Attention Flow for Machine Comprehension arXiv:1611.01603**Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, et al.*2016 · 1,968 citations*](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/) **[Making Pre-trained Language Models Better Few-shot Learners](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Gao, Adam Fisch, Danqi Chen*2021 · 1,963 citations* [**Adversarial Examples for Evaluating Reading Comprehension Systems**Robin Jia, Percy Liang*2017 · 1,942 citations*](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/) **[Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://md.research.elamur.ai/free/en/papers/zhang-2018-personalizing-dialogue-agents-i-have/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Saizheng Zhang, Emily Dinan, Jack Urbanek, et al.*2018 · 1,849 citations* [**PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization arXiv:1912.08777**Jingqing Zhang, Yao Zhao, Mohammad Saleh, et al.*2019 · 1,816 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-pegasus-pre-training-extracted-gap/) [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention arXiv:2006.03654**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,808 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/) [**AllenNLP: A Deep Semantic Natural Language Processing Platform arXiv:1803.07640**Matt Gardner, Joel Grus, Mark Neumann, et al.*2018 · 1,807 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2018-allennlp-deep-semantic-natural-language/) [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,753 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/) [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,703 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/) **[Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://md.research.elamur.ai/free/en/papers/izacard-2021-leveraging-passage-retrieval-generative-models/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Gautier Izacard, Edouard Grave*2021 · 1,675 citations* [**Retrieval-Augmented Generation for Large Language Models: A Survey arXiv:2312.10997**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,648 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/) **[CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://md.research.elamur.ai/free/en/papers/talmor-2019-commonsenseqa-question-answering-challenge-targeting/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Alon Talmor, Jonathan Herzig, Nicholas Lourie, et al.*2019 · 1,639 citations* [**RACE: Large-scale ReAding Comprehension Dataset From Examinations**Guokun Lai, Qizhe Xie, Hanxiao Liu, et al.*2017 · 1,629 citations*](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/) **[Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://md.research.elamur.ai/free/en/papers/yu-2018-spider-large-scale-human-labeled/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tao Yu, Rui Zhang, Kai Yang, et al.*2018 · 1,584 citations* [**Multitask Prompted Training Enables Zero-Shot Task Generalization arXiv:2110.08207**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,581 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/) **[Multi-Task Deep Neural Networks for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/liu-2019-multi-task-deep-neural-networks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaodong Liu, Pengcheng He, Weizhu Chen, et al.*2019 · 1,511 citations* **[The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://md.research.elamur.ai/free/en/papers/joshi-2020-state-fate-linguistic-diversity-inclusion/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pratik Joshi, Sebastin Santy, Amar Budhiraja, et al.*2020 · 1,494 citations* **[LEGAL-BERT: The Muppets straight out of Law School](https://md.research.elamur.ai/free/en/papers/chalkidis-2020-legal-bert-muppets-straight-out/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, et al.*2020 · 1,493 citations* **[Pre-Training with Whole Word Masking for Chinese BERT](https://md.research.elamur.ai/free/en/papers/cui-2019-pre-training-whole-word-masking/) [IEEE/ACM Transactions on Audio Speech and Language Processing](https://md.research.elamur.ai/free/en/venues/ieee-acm-transactions-on-audio-speech-and-language-processing/)**Yiming Cui, Wanxiang Che, Ting Liu, et al.*2019 · 1,490 citations* **[GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhengxiao Du, Yujie Qian, Xiao Liu, et al.*2022 · 1,452 citations* [**Unified Language Model Pre-training for Natural Language Understanding and Generation arXiv:1905.03197**Li Dong, Nan Yang, Wenhui Wang, et al.*2019 · 1,406 citations*](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/) **[Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data](https://md.research.elamur.ai/free/en/papers/bender-2020-climbing-nlu-meaning-form-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Emily M. Bender, Alexander Koller*2020 · 1,375 citations* [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing arXiv:2111.09543**Pengcheng He, Jianfeng Gao, Weizhu Chen*2021 · 1,316 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/) [**AraBERT: Transformer-based Model for Arabic Language Understanding arXiv:2003.00104**Wissam Antoun, Fady Baly, Hazem Hajj*2020 · 1,302 citations*](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/) **[BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://md.research.elamur.ai/free/en/papers/clark-2019-boolq-exploring-surprising-difficulty-natural/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Christopher Clark, Kenton Lee, Ming-Wei Chang, et al.*2019 · 1,301 citations* **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations* [**CTRL: A Conditional Transformer Language Model for Controllable Generation arXiv:1909.05858**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,280 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/) **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,264 citations* **[Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://md.research.elamur.ai/free/en/papers/lee-2019-latent-retrieval-weakly-supervised-open/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kenton Lee, Ming-Wei Chang, Kristina Toutanova*2019 · 1,236 citations* **[PIQA: Reasoning about Physical Commonsense in Natural Language](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yonatan Bisk, Rowan Zellers, Ronan Le Bras, et al.*2019 · 1,220 citations* [**M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation arXiv:2402.03216**Jianlv Chen, Shitao Xiao, Peitian Zhang, et al.*2024 · 1,196 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-m3-embedding-multi-linguality/) **[On the Cross-lingual Transferability of Monolingual Representations](https://md.research.elamur.ai/free/en/papers/artetxe-2020-cross-lingual-transferability-monolingual-representations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mikel Artetxe, Sebastian Ruder, Dani Yogatama*2020 · 1,172 citations* **[PubMedQA: A Dataset for Biomedical Research Question Answering](https://md.research.elamur.ai/free/en/papers/jin-2019-pubmedqa-dataset-biomedical-research-question/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qiao Jin, Bhuwan Dhingra, Zhengping Liu, et al.*2019 · 1,160 citations* [**MASS: Masked Sequence to Sequence Pre-training for Language Generation arXiv:1905.02450**Kaitao Song, Xu Tan, Tao Qin, et al.*2019 · 1,127 citations*](https://md.research.elamur.ai/free/en/papers/song-2019-mass-masked-sequence-sequence-pre/) [**MS MARCO: A Human Generated MAchine Reading COmprehension Dataset arXiv:1611.09268**Payal Bajaj, Daniel Campos, Nick Craswell, et al.*2016 · 1,112 citations*](https://md.research.elamur.ai/free/en/papers/bajaj-2016-ms-marco-human-generated/) **[Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,074 citations* **[QuAC: Question Answering in Context](https://md.research.elamur.ai/free/en/papers/choi-2018-quac-question-answering-context/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Eunsol Choi, He He, Mohit Iyyer, et al.*2018 · 1,062 citations* [**The NarrativeQA Reading Comprehension Challenge**Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom, et al.*2018 · 1,031 citations*](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/) **[P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks](https://md.research.elamur.ai/free/en/papers/liu-2022-p-tuning-prompt-tuning-can/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiao Liu, Kaixuan Ji, Yicheng Fu, et al.*2022 · 1,013 citations* **[MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://md.research.elamur.ai/free/en/papers/sun-2020-mobilebert-compact-task-agnostic-bert/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiqing Sun, Hongkun Yu, Xiaodan Song, et al.*2020 · 1,007 citations* **[FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sewon Min, Kalpesh Krishna, Xinxi Lyu, et al.*2023 · 971 citations* [**GPQA: A Graduate-Level Google-Proof Q&A Benchmark arXiv:2311.12022**David Rein, Betty Li Hou, Asa Cooper Stickland, et al.*2023 · 964 citations*](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/) [**ERNIE: Enhanced Representation through Knowledge Integration arXiv:1904.09223**Yu Sun, Shuohuan Wang, Yukun Li, et al.*2019 · 958 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-ernie-enhanced-representation-through-knowledge/) [**MPNet: Masked and Permuted Pre-training for Language Understanding arXiv:2004.09297**Kaitao Song, Xu Tan, Tao Qin, et al.*2020 · 948 citations*](https://md.research.elamur.ai/free/en/papers/song-2020-mpnet-masked-permuted-pre-training/) [**DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs**Dheeru Dua, Yizhong Wang, Pradeep Dasigi, et al.*2019 · 943 citations*](https://md.research.elamur.ai/free/en/papers/dua-2019-drop-reading-comprehension-benchmark-requiring/) [**Patient Knowledge Distillation for BERT Model Compression arXiv:1908.09355**Siqi Sun, Yu Cheng, Zhe Gan, et al.*2019 · 943 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-patient-knowledge-distillation-bert-model/) **[Learning to Ask: Neural Question Generation for 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