---
title: "Who cites Illia Polosukhin"
description: "18,005 papers of the Elamur Research library cite the works of Illia Polosukhin, found in their bibliographies · page 1 of 12 · 1–100; the most cited first."
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---
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# Who cites Illia Polosukhin

18,396 citations of their works from 18,005 papers of our library (of the 140,884 whose bibliographies we read) · 1,116 with a page here, the most cited first · page 1 of 12 · 1–100 · [← Illia Polosukhin](https://md.research.elamur.ai/free/en/authors/illia-polosukhin/)

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1. [**BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding**Jacob Devlin, Ming-Wei Chang, Kenton Lee, et al.*2019 · 23,422 citations*](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/)
2. [**Transformers: State-of-the-Art Natural Language Processing**Thomas Wolf, Lysandre Debut, Victor Sanh, et al.*2020 · 15,591 citations*](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/)
3. [**BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,752 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/)
4. [**Language Models are Few-Shot Learners**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 10,782 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
5. [**Graph Attention Networks**Petar Veličkovic, Guillem Cucurull, Arantxa Casanova, et al.*2017 · 8,996 citations*](https://md.research.elamur.ai/free/en/papers/veli-vckovic-2017-graph-attention-networks/)
6. [**Learning Transferable Visual Models From Natural Language Supervision**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,295 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
7. [**BioBERT: a pre-trained biomedical language representation model for biomedical text mining**Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, et al.*2019 · 8,281 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-biobert-pre-trained-biomedical-language/)
8. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 7,433 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
9. [**ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education**Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.*2023 · 7,429 citations*](https://md.research.elamur.ai/free/en/papers/kasneci-2023-chatgpt-good-opportunities-challenges-large/)
10. [**Representation Learning with Contrastive Predictive Coding**Aaron van den Oord, Yazhe Li, Oriol Vinyals*2018 · 6,826 citations*](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
11. [**Survey of Hallucination in Natural Language Generation**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,947 citations*](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/)
12. [**Emerging Properties in Self-Supervised Vision Transformers**Mathilde Caron, Hugo Touvron, Ishan Misra, et al.*2021 · 5,910 citations*](https://md.research.elamur.ai/free/en/papers/caron-2021-emerging-properties-self-supervised-vision/)
13. [**LLaMA: Open and Efficient Foundation Language Models**Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al.*2023 · 5,609 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/)
14. [**Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,375 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/)
15. [**Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks**Nils Reimers, Iryna Gurevych*2019 · 4,838 citations*](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/)
16. [**A Call for Clarity in Reporting BLEU Scores**Matt Post*2018 · 4,130 citations*](https://md.research.elamur.ai/free/en/papers/post-2018-call-clarity-reporting-bleu-scores/)
17. [**Energy and Policy Considerations for Deep Learning in NLP**Emma Strubell, Ananya Ganesh, Andrew McCallum*2019 · 4,049 citations*](https://md.research.elamur.ai/free/en/papers/strubell-2019-energy-policy-considerations-deep-learning/)
18. [**fairseq: A Fast, Extensible Toolkit for Sequence Modeling**Myle Ott, Sergey Edunov, Alexei Baevski, et al.*2019 · 3,941 citations*](https://md.research.elamur.ai/free/en/papers/ott-2019-fairseq-fast-extensible-toolkit-sequence/)
19. [**SimCSE: Simple Contrastive Learning of Sentence Embeddings**Tianyu Gao, Xingcheng Yao, Danqi Chen*2021 · 3,921 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/)
20. [**Large language models are zero-shot reasoners**Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.*2022 · 3,780 citations*](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/)
21. [**XLNet: Generalized Autoregressive Pretraining for Language Understanding**Zhilin Yang, Zihang Dai, Yiming Yang, et al.*2019 · 3,740 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/)
22. [**Training Verifiers to Solve Math Word Problems**Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, et al.*2021 · 3,675 citations*](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/)
23. [**A Survey on Evaluation of Large Language Models**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,640 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/)
24. [**SciBERT: A Pretrained Language Model for Scientific Text**Iz Beltagy, Kyle Lo, Arman Cohan*2019 · 3,548 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2019-scibert-pretrained-language-model-scientific/)
25. [**Conformer: Convolution-augmented Transformer for Speech Recognition**Anmol Gulati, James Qin, Chung-Cheng Chiu, et al.*2020 · 3,433 citations*](https://md.research.elamur.ai/free/en/papers/gulati-2020-conformer-convolution-augmented-transformer-speech/)
26. [**On the Opportunities and Risks of Foundation Models**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,044 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
27. [**Language Models as Knowledge Bases?**Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, et al.*2019 · 2,977 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/)
28. [**CodeBERT: A Pre-Trained Model for Programming and Natural Languages**Zhangyin Feng, Daya Guo, Duyu Tang, et al.*2020 · 2,954 citations*](https://md.research.elamur.ai/free/en/papers/feng-2020-codebert-pre-trained-model-programming/)
29. [**Gemini: A Family of Highly Capable Multimodal Models**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 2,951 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
30. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
31. [**Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing**Yu Gu; Robert Tinn; Hao Cheng; Michael Lucas; Naoto Usuyama; Xiaodong Liu; Tristan Naumann; Jianfeng Gao;, Hoifung Poon*2020 · 2,636 citations*](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/)
32. [**PaLM: Scaling Language Modeling with Pathways**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 2,603 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
33. [**Hierarchical Text-Conditional Image Generation with CLIP Latents**Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, et al.*2022 · 2,599 citations*](https://md.research.elamur.ai/free/en/papers/ramesh-2022-hierarchical-text-conditional-image-generation/)
34. [**SpanBERT: Improving Pre-training by Representing and Predicting Spans**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,580 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/)
35. [**Lost in the Middle: How Language Models Use Long Contexts**Nelson F. Liu, Kevin Lin, John Hewitt, et al.*2023 · 2,540 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-lost-middle-language-models-use/)
36. [**Evaluating Large Language Models Trained on Code**Mark Chen, Jerry Tworek, Heewoo Jun, et al.*2021 · 2,529 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-evaluating-large-language-models-trained/)
37. [**Robust Speech Recognition via Large-Scale Weak Supervision**Alec Radford, Jong Wook Kim, Tao Xu, et al.*2022 · 2,497 citations*](https://md.research.elamur.ai/free/en/papers/radford-2022-robust-speech-recognition-large-scale/)
38. [**Sparks of Artificial General Intelligence: Early experiments with GPT-4**Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.*2023 · 2,389 citations*](https://md.research.elamur.ai/free/en/papers/bubeck-2023-sparks-artificial-general-intelligence-early/)
39. [**UNITER: UNiversal Image-TExt Representation Learning**Yen-Chun Chen, Linjie Li, Licheng Yu, et al.*2019 · 2,344 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-uniter-universal-image-text-representation/)
40. [**Measuring Mathematical Problem Solving With the MATH Dataset**Dan Hendrycks, Collin Burns, Saurav Kadavath, et al.*2021 · 2,318 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/)
41. [**Relational inductive biases, deep learning, and graph networks**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,285 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
42. [**Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning**Piyush Sharma, Nan Ding, Sebastian Goodman, et al.*2018 · 2,278 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2018-conceptual-captions-cleaned-hypernymed-image/)
43. [**ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks**Jiasen Lu, Dhruv Batra, Devi Parikh, et al.*2019 · 2,217 citations*](https://md.research.elamur.ai/free/en/papers/lu-2019-vilbert-pretraining-task-agnostic-visiolinguistic/)
44. [**Multimodal Transformer for Unaligned Multimodal Language Sequences**Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, et al.*2019 · 2,170 citations*](https://md.research.elamur.ai/free/en/papers/tsai-2019-multimodal-transformer-unaligned-multimodal-language/)
45. [**Flamingo: a Visual Language Model for Few-Shot Learning**Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, et al.*2022 · 2,108 citations*](https://md.research.elamur.ai/free/en/papers/alayrac-2022-flamingo-visual-language-model-few/)
46. [**TruthfulQA: Measuring How Models Mimic Human Falsehoods**Stephanie Lin, Jacob Hilton, Owain Evans*2022 · 2,047 citations*](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/)
47. [**Toolformer: Language Models Can Teach Themselves to Use Tools**Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, et al.*2023 · 2,020 citations*](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/)
48. [**BERT Rediscovers the Classical NLP Pipeline**Ian Tenney, Dipanjan Das, Ellie Pavlick*2019 · 1,993 citations*](https://md.research.elamur.ai/free/en/papers/tenney-2019-bert-rediscovers-classical-nlp-pipeline/)
49. [**An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale**Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, et al.*2020 · 1,947 citations*](https://md.research.elamur.ai/free/en/papers/dosovitskiy-2020-image-worth-16x16-words-transformers/)
50. [**Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks**Xiujun Li, Xi Yin, Chunyuan Li, et al.*2020 · 1,827 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-oscar-object-semantics-aligned-pre/)
51. [**Scaling Laws for Neural Language Models**Jared Kaplan, Sam McCandlish, Tom Henighan, et al.*2020 · 1,814 citations*](https://md.research.elamur.ai/free/en/papers/kaplan-2020-scaling-laws-neural-language-models/)
52. [**PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization**Jingqing Zhang, Yao Zhao, Mohammad Saleh, et al.*2019 · 1,806 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-pegasus-pre-training-extracted-gap/)
53. [**AllenNLP: A Deep Semantic Natural Language Processing Platform**Matt Gardner, Joel Grus, Mark Neumann, et al.*2018 · 1,804 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2018-allennlp-deep-semantic-natural-language/)
54. [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,800 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/)
55. [**Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference**Timo Schick, Hinrich Schütze*2021 · 1,796 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-exploiting-cloze-questions-few-shot/)
56. [**What Does BERT Learn about the Structure of Language?**Ganesh Jawahar, Benoît Sagot, Djamé Seddah*2019 · 1,789 citations*](https://md.research.elamur.ai/free/en/papers/jawahar-2019-what-does-bert-learn-about/)
57. [**ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT**Omar Khattab, Matei Zaharia*2020 · 1,776 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2020-colbert-efficient-effective-passage/)
58. [**Pre-trained Models for Natural Language Processing: A Survey**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,740 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
59. [**Longformer: The Long-Document Transformer**Iz Beltagy, Matthew E. Peters, Arman Cohan*2020 · 1,737 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2020-longformer-long-document-transformer/)
60. [**OPT: Open Pre-trained Transformer Language Models**Susan Zhang, Stephen Roller, Naman Goyal, et al.*2022 · 1,709 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
61. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,695 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
62. [**Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering**Gautier Izacard, Edouard Grave*2021 · 1,674 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2021-leveraging-passage-retrieval-generative-models/)
63. [**Retrieval-Augmented Generation for Large Language Models: A Survey**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,647 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
64. [**Qwen Technical Report**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,643 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
65. [**Universal Sentence Encoder**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,616 citations*](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder/)
66. [**mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer**Linting Xue, Noah Constant, Adam Roberts, et al.*2021 · 1,574 citations*](https://md.research.elamur.ai/free/en/papers/xue-2021-mt5-massively-multilingual-pre-trained/)
67. [**Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,548 citations*](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/)
68. [**Unifying Large Language Models and Knowledge Graphs: A Roadmap**Shirui Pan, Linhao Luo, Yufei Wang, et al.*2023 · 1,545 citations*](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/)
69. [**Universal Sentence Encoder for English**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,544 citations*](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder-english/)
70. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,522 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
71. [**Efficient Transformers: A Survey**Yi Tay, Mostafa Dehghani, Dara Bahri, et al.*2020 · 1,513 citations*](https://md.research.elamur.ai/free/en/papers/tay-2020-efficient-transformers-survey/)
72. [**Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned**Elena Voita, David Talbot, Fedor Moiseev, et al.*2019 · 1,511 citations*](https://md.research.elamur.ai/free/en/papers/voita-2019-analyzing-multi-head-self-attention/)
73. [**VisualBERT: A Simple and Performant Baseline for Vision and Language**Liunian Harold Li, Mark Yatskar, Da Yin, et al.*2019 · 1,510 citations*](https://md.research.elamur.ai/free/en/papers/li-2019-visualbert-simple-performant-baseline-vision/)
74. [**Multi-Task Deep Neural Networks for Natural Language Understanding**Xiaodong Liu, Pengcheng He, Weizhu Chen, et al.*2019 · 1,501 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-multi-task-deep-neural-networks/)
75. [**CLIPScore: A Reference-free Evaluation Metric for Image Captioning**Jack Hessel, Ari Holtzman, Maxwell Forbes, et al.*2021 · 1,498 citations*](https://md.research.elamur.ai/free/en/papers/hessel-2021-clipscore-reference-free-evaluation-metric/)
76. [**Pre-Training with Whole Word Masking for Chinese BERT**Yiming Cui, Wanxiang Che, Ting Liu, et al.*2019 · 1,485 citations*](https://md.research.elamur.ai/free/en/papers/cui-2019-pre-training-whole-word-masking/)
77. [**Zero-Shot Text-to-Image Generation**Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, et al.*2021 · 1,423 citations*](https://md.research.elamur.ai/free/en/papers/ramesh-2021-zero-shot-text-image-generation/)
78. [**Language-agnostic BERT Sentence Embedding**Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, et al.*2020 · 1,412 citations*](https://md.research.elamur.ai/free/en/papers/feng-2020-language-agnostic-bert-sentence-embedding/)
79. [**LAION-5B: An open large-scale dataset for training next generation image-text models**Christoph Schuhmann, Romain Beaumont, Richard Vencu, et al.*2022 · 1,400 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/)
80. [**Code Llama: Open Foundation Models for Code**Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, et al.*2024 · 1,399 citations*](https://md.research.elamur.ai/free/en/papers/roziere-2024-code-llama-open-foundation-models/)
81. [**Unified Language Model Pre-training for Natural Language Understanding and Generation**Li Dong, Nan Yang, Wenhui Wang, et al.*2019 · 1,389 citations*](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/)
82. [**wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations**Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, et al.*2020 · 1,355 citations*](https://md.research.elamur.ai/free/en/papers/baevski-2020-wav2vec-2-0-framework-self/)
83. [**Parameter-efficient fine-tuning of large-scale pre-trained language models**Ning Ding, Yujia Qin, Guang You Yang, et al.*2023 · 1,324 citations*](https://md.research.elamur.ai/free/en/papers/ding-2023-parameter-efficient-fine-tuning-large/)
84. [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing**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/)
85. [**LaMDA: Language Models for Dialog Applications**Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al.*2022 · 1,314 citations*](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/)
86. [**VideoBERT: A Joint Model for Video and Language Representation Learning**Chen Sun, Austin Myers, Carl Vondrick, et al.*2019 · 1,305 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-videobert-joint-model-video-language/)
87. [**BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions**Christopher Clark, Kenton Lee, Ming-Wei Chang, et al.*2019 · 1,299 citations*](https://md.research.elamur.ai/free/en/papers/clark-2019-boolq-exploring-surprising-difficulty-natural/)
88. [**AraBERT: Transformer-based Model for Arabic Language Understanding**Wissam Antoun, Fady Baly, Hazem Hajj*2020 · 1,297 citations*](https://md.research.elamur.ai/free/en/papers/antoun-2020-arabert-transformer-based-model-arabic/)
89. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
90. [**COMET: Commonsense Transformers for Automatic Knowledge Graph Construction**Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al.*2019 · 1,282 citations*](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/)
91. [**RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,277 citations*](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/)
92. [**CTRL: A Conditional Transformer Language Model for Controllable Generation**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,273 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
93. [**The Rise and Potential of Large Language Model Based Agents: A Survey**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,262 citations*](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/)
94. [**CamemBERT: a Tasty French Language Model**Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, et al.*2019 · 1,262 citations*](https://md.research.elamur.ai/free/en/papers/martin-2019-camembert-tasty-french-language-model/)
95. [**What Makes Good In-Context Examples for GPT-3?**Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al.*2022 · 1,235 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/)
96. [**Latent Retrieval for Weakly Supervised Open Domain Question Answering**Kenton Lee, Ming-Wei Chang, Kristina Toutanova*2019 · 1,234 citations*](https://md.research.elamur.ai/free/en/papers/lee-2019-latent-retrieval-weakly-supervised-open/)
97. [**Recipes for Building an Open-Domain Chatbot**Stephen Roller, Emily Dinan, Naman Goyal, et al.*2021 · 1,206 citations*](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/)
98. [**M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation**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/)
99. [**BERTweet: A pre-trained language model for English Tweets**Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen*2020 · 1,139 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2020-bertweet-pre-trained-language-model/)
100. [**Program Synthesis with Large Language Models**Jacob Austin, Augustus Odena, Maxwell Nye, et al.*2021 · 1,136 citations*](https://md.research.elamur.ai/free/en/papers/austin-2021-program-synthesis-large-language-models/)

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