---
title: "Who cites BERT: Pre-training of Deep Bidirectional…"
description: "23,422 papers of the Elamur Research library cite «BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding», found in their…"
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---
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# Who cites «BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding»

23,422 papers of our library cite it (of the 140,884 whose bibliographies we read) · 1500 with a page here, the most cited first · page 3 of 15 · 201–300 · [← the paper](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/)

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201. [**Passage Re-ranking with BERT**Rodrigo Nogueira, Kyunghyun Cho*2019 · 667 citations*](https://md.research.elamur.ai/free/en/papers/nogueira-2019-passage-re-ranking-bert/)
202. [**BAE: BERT-based Adversarial Examples for Text Classification**Siddhant Garg, Goutham Ramakrishnan*2020 · 665 citations*](https://md.research.elamur.ai/free/en/papers/garg-2020-bae-bert-based-adversarial-examples/)
203. [**KILT: a Benchmark for Knowledge Intensive Language Tasks**Fabio Petroni, Aleksandra Piktus, Angela Fan, et al.*2020 · 663 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/)
204. [**K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters**Ruize Wang, Duyu Tang, Nan Duan, et al.*2020 · 662 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-k-adapter-infusing-knowledge-pre/)
205. [**Efficient Content-Based Sparse Attention with Routing Transformers**Aurko Roy, Mohammad Saffar, Ashish Vaswani, et al.*2020 · 660 citations*](https://md.research.elamur.ai/free/en/papers/roy-2020-efficient-content-based-sparse-attention/)
206. [**CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data**Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, et al.*2020 · 650 citations*](https://md.research.elamur.ai/free/en/papers/wenzek-2020-ccnet-extracting-high-quality-monolingual/)
207. [**ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer**Yuanmeng Yan, Rumei Li, Sirui Wang, et al.*2021 · 647 citations*](https://md.research.elamur.ai/free/en/papers/yan-2021-consert-contrastive-framework-self-supervised/)
208. [**CodeSearchNet Challenge: Evaluating the State of Semantic Code Search**Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, et al.*2020 · 647 citations*](https://md.research.elamur.ai/free/en/papers/husain-2020-codesearchnet-challenge-evaluating-state-semantic/)
209. [**A Survey on Automated Fact-Checking**Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos*2022 · 646 citations*](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/)
210. [**CoCa: Contrastive Captioners are Image-Text Foundation Models**Jiahui Yu, Zirui Wang, Vijay Vasudevan, et al.*2022 · 646 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-coca-contrastive-captioners-image-text/)
211. [**FLAIR: An Easy-to-Use Framework for State-of-the-Art NLP**Alan Akbik, Tanja Bergmann, Duncan Blythe, et al.*2019 · 645 citations*](https://md.research.elamur.ai/free/en/papers/akbik-2019-flair-easy-use-framework-state/)
212. [**A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)**Alane Suhr, Stephanie Zhou, Ally Zhang, et al.*2019 · 640 citations*](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)
213. [**A Frustratingly Easy Approach for Entity and Relation Extraction**Zexuan Zhong, Danqi Chen*2020 · 629 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/)
214. [**Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond**Mikel Artetxe, Holger Schwenk*2018 · 627 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2018-massively-multilingual-sentence-embeddings-zero/)
215. [**LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding**Yang Xu, Yiheng Xu, Tengchao Lv, et al.*2021 · 623 citations*](https://md.research.elamur.ai/free/en/papers/xu-2021-layoutlmv2-multi-modal-pre-training/)
216. [**Scalable Zero-shot Entity Linking with Dense Entity Retrieval**Ledell Wu, Fabio Petroni, Martin Josifoski, et al.*2020 · 618 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-scalable-zero-shot-entity-linking/)
217. [**BloombergGPT: A Large Language Model for Finance**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
218. [**Role-Play with Large Language Models**Murray Shanahan, Kyle McDonell, Laria Reynolds*2023 · 606 citations*](https://md.research.elamur.ai/free/en/papers/shanahan-2023-role-play-large-language-models/)
219. [**Towards a Unified View of Parameter-Efficient Transfer Learning**Junxian He, Chunting Zhou, Xuezhe Ma, et al.*2021 · 604 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-towards-unified-view-parameter-efficient/)
220. [**Event Extraction by Answering (Almost) Natural Questions**Xinya Du, Claire Cardie*2020 · 602 citations*](https://md.research.elamur.ai/free/en/papers/du-2020-event-extraction-answering-almost-natural/)
221. [**A Joint Neural Model for Information Extraction with Global Features**Ying Lin, Heng Ji, Fei Huang, et al.*2020 · 600 citations*](https://md.research.elamur.ai/free/en/papers/lin-2020-joint-neural-model-information-extraction/)
222. [**Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model**Shaden Smith, Mostofa Patwary, Brandon Norick, et al.*2022 · 595 citations*](https://md.research.elamur.ai/free/en/papers/smith-2022-using-deepspeed-megatron-train-megatron/)
223. [**Text Embeddings by Weakly-Supervised Contrastive Pre-training**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2022 · 594 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/)
224. [**FNet: Mixing Tokens with Fourier Transforms**James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, et al.*2022 · 593 citations*](https://md.research.elamur.ai/free/en/papers/thorp-2022-fnet-mixing-tokens-fourier-transforms/)
225. [**WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations**Mohammad Taher Pilehvar, Jose Camacho-Collados*2019 · 592 citations*](https://md.research.elamur.ai/free/en/papers/pilehvar-2019-wic-word-context-dataset-evaluating/)
226. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 590 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/)
227. [**A Robustly Optimized BERT Pre-training Approach with Post-training**Liu Zhuang, Lin Wayne, Shi Ya, et al.*2021 · 585 citations*](https://md.research.elamur.ai/free/en/papers/zhuang-2021-robustly-optimized-bert-pre-training/)
228. [**Measuring Bias in Contextualized Word Representations**Keita Kurita, Nidhi Vyas, Ayush Pareek, et al.*2019 · 584 citations*](https://md.research.elamur.ai/free/en/papers/kurita-2019-measuring-bias-contextualized-word-representations/)
229. [**PAWS: Paraphrase Adversaries from Word Scrambling**Yuan Zhang, Jason Baldridge, Luheng He*2019 · 584 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-paws-paraphrase-adversaries-word-scrambling/)
230. [**Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models**Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, et al.*2021 · 582 citations*](https://md.research.elamur.ai/free/en/papers/ni-2021-sentence-t5-scalable-sentence-encoders/)
231. [**What do you learn from context? Probing for sentence structure in contextualized word representations**Ian Tenney, Patrick Xia, Berlin Chen, et al.*2019 · 582 citations*](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/)
232. [**OCR-free Document Understanding Transformer**Geewook Kim, Teakgyu Hong, Moonbin Yim, et al.*2021 · 580 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-ocr-free-document-understanding-transformer/)
233. [**PhoBERT: Pre-trained language models for Vietnamese**Dat Quoc Nguyen, Anh Tuan Nguyen*2020 · 571 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2020-phobert-pre-trained-language-models/)
234. [**AudioLM: a Language Modeling Approach to Audio Generation**Zalan Borsos, Raphael Marinier, Damien Vincent, et al.*2022 · 570 citations*](https://md.research.elamur.ai/free/en/papers/borsos-2022-audiolm-language-modeling-approach-audio/)
235. [**Extractive Summarization as Text Matching**Ming Zhong, Pengfei Liu, Yiran Chen, et al.*2020 · 567 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2020-extractive-summarization-as-text-matching/)
236. [**Learning How to Ask: Querying LMs with Mixtures of Soft Prompts**Guanghui Qin, Jason Eisner*2021 · 566 citations*](https://md.research.elamur.ai/free/en/papers/qin-2021-learning-ask-querying-lms-mixtures/)
237. [**ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs**Yujia Qin, Shihao Liang, Yining Ye, et al.*2023 · 564 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-toolllm-facilitating-large-language-models/)
238. [**AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models**Wanjun Zhong, Ruixiang Cui, Yiduo Guo, et al.*2024 · 563 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-agieval-human-centric-benchmark-evaluating/)
239. [**MIND: A Large-scale Dataset for News Recommendation**Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, et al.*2020 · 562 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-mind-large-scale-dataset-news/)
240. [**Leveraging Pre-trained Checkpoints for Sequence Generation Tasks**Sascha Rothe, Shashi Narayan, Aliaksei Severyn*2019 · 562 citations*](https://md.research.elamur.ai/free/en/papers/rothe-2019-leveraging-pre-trained-checkpoints-sequence/)
241. [**Plug and Play Language Models: A Simple Approach to Controlled Text Generation**Sumanth Dathathri, Andrea Madotto, Janice Lan, et al.*2019 · 562 citations*](https://md.research.elamur.ai/free/en/papers/dathathri-2019-plug-play-language-models-simple/)
242. [**Neural Legal Judgment Prediction in English**Ilias Chalkidis, Ion Androutsopoulos, Nikolaos Aletras*2019 · 558 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2019-neural-legal-judgment-prediction-english/)
243. [**MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models**Longhui Yu, Weisen Jiang, Han Shi, et al.*2023 · 556 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/)
244. [**Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges**Naveen Arivazhagan, Ankur Bapna, Orhan Firat, et al.*2019 · 555 citations*](https://md.research.elamur.ai/free/en/papers/arivazhagan-2019-massively-multilingual-neural-machine-translation/)
245. [**XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization**Junjie Hu, Sebastian Ruder, Aditya Siddhant, et al.*2020 · 551 citations*](https://md.research.elamur.ai/free/en/papers/hu-2020-xtreme-massively-multilingual-multi-task/)
246. [**Reducing Transformer Depth on Demand with Structured Dropout**Angela Fan, Edouard Grave, Armand Joulin*2019 · 546 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/)
247. [**Precise Zero-Shot Dense Retrieval without Relevance Labels**Luyu Gao, Xueguang Ma, Jimmy Lin, et al.*2022 · 545 citations*](https://md.research.elamur.ai/free/en/papers/gao-2022-precise-zero-shot-dense-retrieval/)
248. [**BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis**Hu Xu, Bing Liu, Lei Shu, et al.*2019 · 544 citations*](https://md.research.elamur.ai/free/en/papers/xu-2019-bert-post-training-review-reading/)
249. [**What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams**Di Jin, Eileen Pan, Nassim Oufattole, et al.*2020 · 540 citations*](https://md.research.elamur.ai/free/en/papers/jin-2020-disease-this-patient-have-large/)
250. [**End-to-End Open-Domain Question Answering with BERTserini**Wei Yang, Yuqing Xie, Aileen Lin, et al.*2019 · 539 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-end-end-open-domain-question/)
251. [**Document Ranking with a Pretrained Sequence-to-Sequence Model**Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, et al.*2020 · 538 citations*](https://md.research.elamur.ai/free/en/papers/nogueira-2020-document-ranking-pretrained-sequence-sequence/)
252. [**Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning**Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, et al.*2019 · 536 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/)
253. [**Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning**Armen Aghajanyan, Sonal Gupta, Luke Zettlemoyer*2021 · 530 citations*](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-intrinsic-dimensionality-explains-effectiveness-language/)
254. [**The Second Conversational Intelligence Challenge (ConvAI2)**Emily Dinan, Varvara Logacheva, Valentin Malykh, et al.*2019 · 525 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2019-second-conversational-intelligence-challenge-convai2/)
255. [**Simple, Scalable Adaptation for Neural Machine Translation**Ankur Bapna, Orhan Firat*2019 · 524 citations*](https://md.research.elamur.ai/free/en/papers/bapna-2019-simple-scalable-adaptation-neural-machine/)
256. [**Evaluating Models' Local Decision Boundaries via Contrast Sets**Matt Gardner, Yoav Artzi, Victoria Basmov, et al.*2020 · 520 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/)
257. [**How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence**Haoxi Zhong, Chaojun Xiao, Cunchao Tu, et al.*2020 · 518 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2020-how-does-nlp-benefit-legal/)
258. [**Instruction Tuning for Large Language Models: A Survey**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 515 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/)
259. [**Probing Classifiers: Promises, Shortcomings, and Advances**Yonatan Belinkov*2022 · 513 citations*](https://md.research.elamur.ai/free/en/papers/belinkov-2022-probing-classifiers-promises-shortcomings-advances/)
260. [**DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations**John Giorgi, Osvald Nitski, Bo Wang, et al.*2021 · 513 citations*](https://md.research.elamur.ai/free/en/papers/giorgi-2021-declutr-deep-contrastive-learning-unsupervised/)
261. [**FinQA: A Dataset of Numerical Reasoning over Financial Data**Zhiyu Chen, Wenhu Chen, Charese Smiley, et al.*2021 · 510 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-finqa-dataset-numerical-reasoning-over/)
262. [**SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking**Thibault Formal, Benjamin Piwowarski, Stéphane Clinchant*2021 · 510 citations*](https://md.research.elamur.ai/free/en/papers/formal-2021-splade-sparse-lexical-expansion/)
263. [**Assessing BERT's Syntactic Abilities**Yoav Goldberg*2019 · 509 citations*](https://md.research.elamur.ai/free/en/papers/goldberg-2019-assessing-bert-s-syntactic-abilities/)
264. [**TabFact: A Large-scale Dataset for Table-based Fact Verification**Wenhu Chen, Hongmin Wang, Jianshu Chen, et al.*2019 · 508 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/)
265. [**Gender Bias in Contextualized Word Embeddings**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2019 · 503 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2019-gender-bias-contextualized-word-embeddings/)
266. [**Sparse, Dense, and Attentional Representations for Text Retrieval**Yi Luan, Jacob Eisenstein, Kristina Toutanova, et al.*2021 · 500 citations*](https://md.research.elamur.ai/free/en/papers/luan-2021-sparse-dense-attentional-representations-text/)
267. [**Few-shot Learning with Multilingual Language Models**Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, et al.*2021 · 496 citations*](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/)
268. [**A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge**Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, et al.*2022 · 490 citations*](https://md.research.elamur.ai/free/en/papers/schwenk-2022-okvqa-benchmark-visual-question-answering/)
269. [**Language Models are Multilingual Chain-of-Thought Reasoners**Freda Shi, Mirac Suzgun, Markus Freitag, et al.*2022 · 490 citations*](https://md.research.elamur.ai/free/en/papers/shi-2022-language-models-multilingual-chain-thought/)
270. [**Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks**Jason Phang, Thibault Févry, Samuel R. Bowman*2018 · 490 citations*](https://md.research.elamur.ai/free/en/papers/phang-2018-sentence-encoders-stilts-supplementary-training/)
271. [**BERT for Coreference Resolution: Baselines and Analysis**Mandar Joshi, Omer Levy, Luke Zettlemoyer, et al.*2019 · 488 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2019-bert-coreference-resolution-baselines-analysis/)
272. [**DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference**Ji Xin, Raphael Tang, Jaejun Lee, et al.*2020 · 480 citations*](https://md.research.elamur.ai/free/en/papers/xin-2020-deebert-dynamic-early-exiting-accelerating/)
273. [**Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection**Shauli Ravfogel, Yanai Elazar, Hila Gonen, et al.*2020 · 480 citations*](https://md.research.elamur.ai/free/en/papers/ravfogel-2020-null-it-out-guarding-protected/)
274. [**ToTTo: A Controlled Table-To-Text Generation Dataset**Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, et al.*2020 · 479 citations*](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/)
275. [**ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic**Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi*2021 · 478 citations*](https://md.research.elamur.ai/free/en/papers/abdul-mageed-2021-arbert-marbert-deep-bidirectional-transformers/)
276. [**ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction**Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al.*2022 · 477 citations*](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/)
277. [**DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature**Eric Mitchell, Yoonho Lee, Alexander Khazatsky, et al.*2023 · 474 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2023-detectgpt-zero-shot-machine-generated/)
278. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 474 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
279. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 474 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
280. [**Data Augmentation using Pre-trained Transformer Models**Varun Kumar, Ashutosh Choudhary, Eunah Cho*2020 · 468 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2020-data-augmentation-using-pre-trained/)
281. [**PPT: Pre-trained Prompt Tuning for Few-shot Learning**Yuxian Gu, Xu Han, Zhiyuan Liu, et al.*2022 · 467 citations*](https://md.research.elamur.ai/free/en/papers/gu-2022-ppt-pre-trained-prompt-tuning/)
282. [**Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT**Shijie Wu, Mark Dredze*2019 · 466 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-beto-bentz-becas-surprising-cross/)
283. [**XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning**Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, et al.*2020 · 465 citations*](https://md.research.elamur.ai/free/en/papers/ponti-2020-xcopa-multilingual-dataset-causal-commonsense/)
284. [**P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks**Xiao Liu, Kaixuan Ji, Yicheng Fu, et al.*2021 · 464 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-p-tuning-v2-prompt-tuning/)
285. [**Masked Autoencoders Are Scalable Vision Learners**Kaiming He, Xinlei Chen, Saining Xie, et al.*2021 · 463 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-masked-autoencoders-scalable-vision-learners/)
286. [**Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation**Dawei Gao, Haibin Wang, Yaliang Li, et al.*2023 · 461 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-text-sql-empowered-large-language/)
287. [**From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers**Anne Lauscher, Vinit Ravishankar, Ivan Vulić, et al.*2020 · 461 citations*](https://md.research.elamur.ai/free/en/papers/lauscher-2020-zero-hero-limitations-zero-shot/)
288. [**Defending Against Neural Fake News**Rowan Zellers, Ari Holtzman, Hannah Rashkin, et al.*2019 · 461 citations*](https://md.research.elamur.ai/free/en/papers/zellers-2019-defending-against-neural-fake-news/)
289. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 459 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/)
290. [**Are All Languages Created Equal in Multilingual BERT?**Shijie Wu, Mark Dredze*2020 · 458 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-are-all-languages-created-equal/)
291. [**Exploring Pre-trained Language Models for Event Extraction and Generation**Sen Yang, Dawei Feng, Linbo Qiao, et al.*2019 · 454 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-exploring-pre-trained-language-models/)
292. [**LinkBERT: Pretraining Language Models with Document Links**Michihiro Yasunaga, Jure Leskovec, Percy Liang*2022 · 452 citations*](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/)
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