---
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 6 of 15 · 501–600 · [← the paper](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/)

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501. [**AlignScore: Evaluating Factual Consistency with A Unified Alignment Function**Yuheng Zha, Yichi Yang, Ruichen Li, et al.*2023 · 280 citations*](https://md.research.elamur.ai/free/en/papers/zha-2023-alignscore-evaluating-factual-consistency-unified/)
502. [**Ragas: Automated Evaluation of Retrieval Augmented Generation**Shahul Es, Jithin James, Luis Espinosa-Anke, et al.*2023 · 280 citations*](https://md.research.elamur.ai/free/en/papers/es-2023-ragas-automated-evaluation-retrieval-augmented/)
503. [**Generate rather than Retrieve: Large Language Models are Strong Context Generators**Wenhao Yu, Dan Iter, Shuohang Wang, et al.*2022 · 280 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-generate-rather-than-retrieve-large/)
504. [**ONION: A Simple and Effective Defense Against Textual Backdoor Attacks**Fanchao Qi, Yangyi Chen, Mukai Li, et al.*2021 · 279 citations*](https://md.research.elamur.ai/free/en/papers/qi-2021-onion-simple-effective-defense-against/)
505. [**Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?**Yada Pruksachatkun, Jason Phang, Haokun Liu, et al.*2020 · 279 citations*](https://md.research.elamur.ai/free/en/papers/pruksachatkun-2020-intermediate-task-transfer-learning-pretrained/)
506. [**BAM! Born-Again Multi-Task Networks for Natural Language Understanding**Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, et al.*2019 · 279 citations*](https://md.research.elamur.ai/free/en/papers/clark-2019-bam-born-again-multi-task/)
507. [**OpenPrompt: An Open-source Framework for Prompt-learning**Ning Ding, Shengding Hu, Weilin Zhao, et al.*2022 · 278 citations*](https://md.research.elamur.ai/free/en/papers/ding-2022-openprompt-open-source-framework-prompt/)
508. [**RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL**Haoyang Li, Jing Zhang, Cuiping Li, et al.*2023 · 277 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-resdsql-decoupling-schema-linking-skeleton/)
509. [**XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation**Sebastian Ruder, Noah Constant, Jan Botha, et al.*2021 · 277 citations*](https://md.research.elamur.ai/free/en/papers/ruder-2021-xtreme-r-more-challenging-nuanced/)
510. [**MIME: MIMicking Emotions for Empathetic Response Generation**Navonil Majumder, Pengfei Hong, Shanshan Peng, et al.*2020 · 277 citations*](https://md.research.elamur.ai/free/en/papers/majumder-2020-mime-mimicking-emotions-empathetic-response/)
511. [**TRUE: Re-evaluating Factual Consistency Evaluation**Or Honovich, Roee Aharoni, Jonathan Herzig, et al.*2022 · 276 citations*](https://md.research.elamur.ai/free/en/papers/honovich-2022-true-re-evaluating-factual-consistency/)
512. [**Large Dual Encoders Are Generalizable Retrievers**Jianmo Ni, Chen Qu, Jing Lu, et al.*2021 · 276 citations*](https://md.research.elamur.ai/free/en/papers/ni-2021-large-dual-encoders-generalizable-retrievers/)
513. [**LLM-QAT: Data-Free Quantization Aware Training for Large Language Models**Zechun Liu, Barlas Oguz, Changsheng Zhao, et al.*2024 · 275 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-llm-qat-data-free-quantization/)
514. [**Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks**Nandan Thakur, Nils Reimers, Johannes Daxenberger, et al.*2021 · 275 citations*](https://md.research.elamur.ai/free/en/papers/thakur-2021-augmented-sbert-data-augmentation-method/)
515. [**Text Classification Using Label Names Only: A Language Model Self-Training Approach**Yu Meng, Yunyi Zhang, Jiaxin Huang, et al.*2020 · 274 citations*](https://md.research.elamur.ai/free/en/papers/meng-2020-text-classification-label-names-only/)
516. [**A Diagnostic Study of Explainability Techniques for Text Classification**Pepa Atanasova, Jakob Grue Simonsen, Christina Lioma, et al.*2020 · 273 citations*](https://md.research.elamur.ai/free/en/papers/atanasova-2020-diagnostic-study-explainability-techniques-text/)
517. [**XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond**Francesco Barbieri, Luis Espinosa Anke, Jose Camacho-Collados*2022 · 271 citations*](https://md.research.elamur.ai/free/en/papers/barbieri-2022-xlm-t-multilingual-language-models/)
518. [**Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering**Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, et al.*2019 · 271 citations*](https://md.research.elamur.ai/free/en/papers/asai-2019-learning-retrieve-reasoning-paths-over/)
519. [**Adaptively Sparse Transformers**Gonçalo M. Correia, Vlad Niculae, André F. T. Martins*2019 · 270 citations*](https://md.research.elamur.ai/free/en/papers/correia-2019-adaptively-sparse-transformers/)
520. [**DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings**Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, et al.*2022 · 269 citations*](https://md.research.elamur.ai/free/en/papers/chuang-2022-diffcse-difference-based-contrastive-learning/)
521. [**InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training**Zewen Chi, Li Dong, Furu Wei, et al.*2020 · 269 citations*](https://md.research.elamur.ai/free/en/papers/chi-2020-infoxlm-information-theoretic-framework-cross/)
522. [**Having Beer after Prayer? Measuring Cultural Bias in Large Language Models**Tarek Naous, Michael J. Ryan, Alan Ritter, et al.*2023 · 268 citations*](https://md.research.elamur.ai/free/en/papers/naous-2023-having-beer-after-prayer-measuring/)
523. [**An Explanation of In-context Learning as Implicit Bayesian Inference**Sang Michael Xie, Aditi Raghunathan, Percy Liang, et al.*2021 · 268 citations*](https://md.research.elamur.ai/free/en/papers/xie-2021-explanation-context-learning-implicit-bayesian/)
524. [**Attention is Not Only a Weight: Analyzing Transformers with Vector Norms**Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, et al.*2020 · 268 citations*](https://md.research.elamur.ai/free/en/papers/kobayashi-2020-attention-is-not-only-weight/)
525. [**Efficient Dialogue State Tracking by Selectively Overwriting Memory**Sungdong Kim, Sohee Yang, Gyuwan Kim, et al.*2020 · 268 citations*](https://md.research.elamur.ai/free/en/papers/kim-2020-efficient-dialogue-state-tracking-selectively/)
526. [**Injecting Numerical Reasoning Skills into Language Models**Mor Geva, Ankit Gupta, Jonathan Berant*2020 · 268 citations*](https://md.research.elamur.ai/free/en/papers/geva-2020-injecting-numerical-reasoning-skills-into/)
527. [**Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations**Emily Allaway, Kathleen McKeown*2020 · 268 citations*](https://md.research.elamur.ai/free/en/papers/allaway-2020-zero-shot-stance-detection-dataset/)
528. [**GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation**Kang Min Yoo, Dongju Park, Jaewook Kang, et al.*2021 · 267 citations*](https://md.research.elamur.ai/free/en/papers/yoo-2021-gpt3mix-leveraging-large-scale-language/)
529. [**ConveRT: Efficient and Accurate Conversational Representations from Transformers**Matthew Henderson, Iñigo Casanueva, Nikola Mrkšić, et al.*2020 · 267 citations*](https://md.research.elamur.ai/free/en/papers/henderson-2020-convert-efficient-accurate-conversational-representations/)
530. [**CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review**Dan Hendrycks, Collin Burns, Anya Chen, et al.*2021 · 265 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-cuad-expert-annotated-nlp-dataset/)
531. [**Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network**Yutai Hou, Wanxiang Che, Yongkui Lai, et al.*2020 · 265 citations*](https://md.research.elamur.ai/free/en/papers/hou-2020-few-shot-slot-tagging-collapsed/)
532. [**IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding**Bryan Wilie, Karissa Vincentio, Genta Indra Winata, et al.*2020 · 265 citations*](https://md.research.elamur.ai/free/en/papers/wilie-2020-indonlu-benchmark-resources-evaluating-indonesian/)
533. [**An Analysis of Simple Data Augmentation for Named Entity Recognition**Xiang Dai, Heike Adel*2020 · 264 citations*](https://md.research.elamur.ai/free/en/papers/dai-2020-analysis-simple-data-augmentation-named/)
534. [**Evaluating the Underlying Gender Bias in Contextualized Word Embeddings**Christine Basta, Marta R. Costa-jussà, Noe Casas*2019 · 264 citations*](https://md.research.elamur.ai/free/en/papers/basta-2019-evaluating-underlying-gender-bias-contextualized/)
535. [**Towards Understanding and Mitigating Social Biases in Language Models**Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, et al.*2021 · 263 citations*](https://md.research.elamur.ai/free/en/papers/liang-2021-towards-understanding-mitigating-social-biases/)
536. [**BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models**Nandan Thakur, Nils Reimers, Andreas Rücklé, et al.*2021 · 262 citations*](https://md.research.elamur.ai/free/en/papers/thakur-2021-beir-heterogenous-benchmark-zero/)
537. [**Condenser: a Pre-training Architecture for Dense Retrieval**Luyu Gao, Jamie Callan*2021 · 261 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-condenser-pre-training-architecture-dense/)
538. [**Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing**Xi Victoria Lin, Richard Socher, Caiming Xiong*2020 · 261 citations*](https://md.research.elamur.ai/free/en/papers/lin-2020-bridging-textual-tabular-data-cross/)
539. [**Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks**Haoyang Huang, Yaobo Liang, Nan Duan, et al.*2019 · 261 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-unicoder-universal-language-encoder-pre/)
540. [**SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!**Chhavi Sharma, Deepesh Bhageria, William Scott, et al.*2020 · 260 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2020-semeval-2020-task-8-memotion/)
541. [**TLDR: Extreme Summarization of Scientific Documents**Isabel Cachola, Kyle Lo, Arman Cohan, et al.*2020 · 260 citations*](https://md.research.elamur.ai/free/en/papers/cachola-2020-tldr-extreme-summarization-scientific-documents/)
542. [**Large Language Models for Data Annotation and Synthesis: A Survey**Zhen Tan, Dawei Li, Song Wang, et al.*2024 · 259 citations*](https://md.research.elamur.ai/free/en/papers/tan-2024-large-language-models-data-annotation/)
543. [**Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing**Brian Thompson, Matt Post*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/thompson-2020-automatic-machine-translation-evaluation-many/)
544. [**MAVEN: A Massive General Domain Event Detection Dataset**Xiaozhi Wang, Ziqi Wang, Xu Han, et al.*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-maven-massive-general-domain-event/)
545. [**On Extractive and Abstractive Neural Document Summarization with Transformer Language Models**Jonathan Pilault, Raymond Li, Sandeep Subramanian, et al.*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/pilault-2020-extractive-abstractive-neural-document-summarization/)
546. [**Towards Debiasing Sentence Representations**Paul Pu Liang, Irene Mengze Li, Emily Zheng, et al.*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/liang-2020-debiasing-sentence-representations/)
547. [**CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization**Shuyang Cao, Lu Wang*2021 · 257 citations*](https://md.research.elamur.ai/free/en/papers/cao-2021-cliff-contrastive-learning-improving-faithfulness/)
548. [**How Can We Accelerate Progress Towards Human-like Linguistic Generalization?**Tal Linzen*2020 · 257 citations*](https://md.research.elamur.ai/free/en/papers/linzen-2020-how-can-we-accelerate-progress/)
549. [**SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking**Hwaran Lee, Jinsik Lee, Tae-Yoon Kim*2019 · 257 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-sumbt-slot-utterance-matching-universal/)
550. [**Contrastive Learning for Many-to-many Multilingual Neural Machine Translation**Xiao Pan, Mingxuan Wang, Liwei Wu, et al.*2021 · 256 citations*](https://md.research.elamur.ai/free/en/papers/pan-2021-contrastive-learning-many-many-multilingual/)
551. [**LexGLUE: A Benchmark Dataset for Legal Language Understanding in English**Ilias Chalkidis; Abhik Jana; Dirk Hartung; Michael Bommarito; Ion Androutsopoulos; Daniel Martin Katz;, Nikolaos Aletras*2021 · 256 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-lexglue-benchmark-dataset-legal-language/)
552. [**RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking**Ruiyang Ren, Yingqi Qu, Jing Liu, et al.*2021 · 256 citations*](https://md.research.elamur.ai/free/en/papers/ren-2021-rocketqav2-joint-training-method-dense/)
553. [**Multilingual is not enough: BERT for Finnish**Antti Virtanen, Jenna Kanerva, Rami Ilo, et al.*2019 · 256 citations*](https://md.research.elamur.ai/free/en/papers/virtanen-2019-multilingual-enough-bert-finnish/)
554. [**Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning**Pradeep Dasigi, Nelson F. Liu, Ana Marasovic, et al.*2019 · 256 citations*](https://md.research.elamur.ai/free/en/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/)
555. [**Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions**Hongjie Cai, Rui Xia, Jianfei Yu*2021 · 255 citations*](https://md.research.elamur.ai/free/en/papers/cai-2021-aspect-category-opinion-sentiment-quadruple/)
556. [**The Multilingual Amazon Reviews Corpus**Phillip Keung, Yichao Lu, György Szarvas, et al.*2020 · 255 citations*](https://md.research.elamur.ai/free/en/papers/keung-2020-multilingual-amazon-reviews-corpus/)
557. [**Fine-Tuning LLaMA for Multi-Stage Text Retrieval**Xueguang Ma, Liang Wang, Nan Yang, et al.*2023 · 254 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-fine-tuning-llama-multi-stage/)
558. [**A Holistic Approach to Undesired Content Detection in the Real World**Todor Markov, Chong Zhang, Sandhini Agarwal, et al.*2022 · 254 citations*](https://md.research.elamur.ai/free/en/papers/markov-2022-holistic-approach-undesired-content-detection/)
559. [**DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale**Samyam Rajbhandari, Conglong Li, Zhewei Yao, et al.*2022 · 254 citations*](https://md.research.elamur.ai/free/en/papers/rajbhandari-2022-deepspeed-moe-advancing-mixture-experts/)
560. [**TAPEX: Table Pre-training via Learning a Neural SQL Executor**Qian Liu, Bei Chen, Jiaqi Guo, et al.*2021 · 254 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-tapex-table-pre-training-learning/)
561. [**Unlearn Dataset Bias in Natural Language Inference by Fitting the Residual**He He, Sheng Zha, Haohan Wang*2019 · 254 citations*](https://md.research.elamur.ai/free/en/papers/he-2019-unlearn-dataset-bias-natural-language/)
562. [**An Empirical Survey of Data Augmentation for Limited Data Learning in NLP**Jiaao Chen, Derek Tam, Colin Raffel, et al.*2023 · 253 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-empirical-survey-data-augmentation-limited/)
563. [**GPT-RE: In-context Learning for Relation Extraction using Large Language Models**Zhen Wan, Fei Cheng, Zhuoyuan Mao, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-gpt-re-context-learning-relation/)
564. [**Societal Biases in Language Generation: Progress and Challenges**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2021 · 252 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2021-societal-biases-language-generation-progress/)
565. [**Measuring and Reducing Gendered Correlations in Pre-trained Models**Kellie Webster, Xuezhi Wang, Ian Tenney, et al.*2020 · 252 citations*](https://md.research.elamur.ai/free/en/papers/webster-2020-measuring-reducing-gendered-correlations-pre/)
566. [**NULI at SemEval-2019 Task 6: Transfer Learning for Offensive Language Detection using Bidirectional Transformers**Ping Liu, Wen Li, Liang Zou*2019 · 252 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-nuli-semeval-2019-task-6/)
567. [**ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation**Vijit Malik, Rishabh Sanjay, Shubham Kumar Nigam, et al.*2021 · 251 citations*](https://md.research.elamur.ai/free/en/papers/malik-2021-ildc-cjpe-indian-legal-documents/)
568. [**The Curse of Recursion: Training on Generated Data Makes Models Forget**Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, et al.*2023 · 250 citations*](https://md.research.elamur.ai/free/en/papers/shumailov-2023-curse-recursion-training-generated-data/)
569. [**Generating Datasets with Pretrained Language Models**Timo Schick, Hinrich Schutze*2021 · 250 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/)
570. [**Event Extraction as Multi-turn Question Answering**Fayuan Li, Weihua Peng, Yuguang Chen, et al.*2020 · 250 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-event-extraction-as-multi-turn/)
571. [**BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model**Alex Wang, Kyunghyun Cho*2019 · 249 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-bert-has-mouth-must-speak/)
572. [**Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings**Rishi Bommasani, Kelly Davis, Claire Cardie*2020 · 248 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/)
573. [**DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 246 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/)
574. [**CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts**Ayah Zirikly, Philip Resnik, Özlem Uzuner, et al.*2019 · 246 citations*](https://md.research.elamur.ai/free/en/papers/zirikly-2019-clpsych-2019-shared-task-predicting/)
575. [**SocialIQA: Commonsense Reasoning about Social Interactions**Maarten Sap, Hannah Rashkin, Derek Chen, et al.*2019 · 246 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-socialiqa-commonsense-reasoning-about-social/)
576. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 245 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
577. [**Ignore Previous Prompt: Attack Techniques For Language Models**Fabio Perez, Ian Ribeiro*2022 · 244 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-ignore-previous-prompt-attack-techniques/)
578. [**Structured Denoising Diffusion Models in Discrete State-Spaces**Jacob Austin, Daniel D. Johnson, Jonathan Ho, et al.*2021 · 244 citations*](https://md.research.elamur.ai/free/en/papers/austin-2021-structured-denoising-diffusion-models-discrete/)
579. [**Knowledge-Grounded Dialogue Generation with Pre-trained Language Models**Xueliang Zhao, Wei Wu, Can Xu, et al.*2020 · 244 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2020-knowledge-grounded-dialogue-generation-pre/)
580. [**Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT**Zhiyong Wu, Yun Chen, Ben Kao, et al.*2020 · 244 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-perturbed-masking-parameter-free-probing/)
581. [**Can You Unpack That? Learning to Rewrite Questions-in-Context**Ahmed Elgohary, Denis Peskov, Jordan Boyd-Graber*2019 · 244 citations*](https://md.research.elamur.ai/free/en/papers/elgohary-2019-can-you-unpack-that-learning/)
582. [**MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers**Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al.*2021 · 243 citations*](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/)
583. [**Automatic Detection of Machine Generated Text: A Critical Survey**Ganesh Jawahar, Muhammad Abdul-Mageed, Laks Lakshmanan V.S.*2020 · 243 citations*](https://md.research.elamur.ai/free/en/papers/jawahar-2020-automatic-detection-machine-generated-text/)
584. [**Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations**Zhuoyan Li, Hangxiao Zhu, Zhuoran Lu, et al.*2023 · 242 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-synthetic-data-generation-large-language/)
585. [**CLUE: A Chinese Language Understanding Evaluation Benchmark**Liang Xu, Hai Hu, Xuanwei Zhang, et al.*2020 · 242 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-clue-chinese-language-understanding/)
586. [**Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling**Xiaochuang Han, Jacob Eisenstein*2019 · 242 citations*](https://md.research.elamur.ai/free/en/papers/han-2019-unsupervised-domain-adaptation-contextualized-embeddings/)
587. [**In-context Examples Selection for Machine Translation**Sweta Agrawal, Chunting Zhou, Mike Lewis, et al.*2023 · 241 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2023-context-examples-selection-machine-translation/)
588. [**A review of sentiment analysis research in Arabic language**Oumaima Oueslati; Erik Cambria; Moez Ben HajHmida;, Habib Ounelli*2020 · 241 citations*](https://md.research.elamur.ai/free/en/papers/oueslati-2020-review-sentiment-analysis-research-arabic/)
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590. [**SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets**Parth Patwa, Gustavo Aguilar, Sudipta Kar, et al.*2020 · 240 citations*](https://md.research.elamur.ai/free/en/papers/patwa-2020-semeval-2020-task-9-overview/)
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