---
title: "Who cites Yanqi Zhou"
description: "8,127 papers of the Elamur Research library cite the works of Yanqi Zhou, found in their bibliographies · page 1 of 7 · 1–100; the most cited first."
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---
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# Who cites Yanqi Zhou

8,538 citations of their works from 8,127 papers of our library (of the 140,884 whose bibliographies we read) · 669 with a page here, the most cited first · page 1 of 7 · 1–100 · [← Yanqi Zhou](https://md.research.elamur.ai/free/en/authors/yanqi-zhou/)

**1** [2](https://md.research.elamur.ai/free/en/authors/yanqi-zhou/cited-by/page/2/) … [7](https://md.research.elamur.ai/free/en/authors/yanqi-zhou/cited-by/page/7/) [next →](https://md.research.elamur.ai/free/en/authors/yanqi-zhou/cited-by/page/2/)

1. [**Swin Transformer: Hierarchical Vision Transformer using Shifted Windows**Ze Liu, Yutong Lin, Yue Cao, et al.*2021 · 33,826 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-swin-transformer-hierarchical-vision-transformer/)
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. [**Chain-of-Thought Prompting Elicits Reasoning in Large Language Models**Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.*2022 · 11,278 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
5. [**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/)
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. [**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/)
8. [**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/)
9. [**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/)
10. [**Training language models to follow instructions with human feedback**Long Ouyang, Jeff Wu, Xu Jiang, et al.*2022 · 5,770 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
11. [**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/)
12. [**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/)
13. [**Dense Passage Retrieval for Open-Domain Question Answering**Vladimir Karpukhin, Barlas Oguz, Sewon Min, et al.*2020 · 5,370 citations*](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/)
14. [**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/)
15. [**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/)
16. [**Measuring Massive Multitask Language Understanding**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/)
17. [**A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,221 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/)
18. [**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/)
19. [**Self-Consistency Improves Chain of Thought Reasoning in Language Models**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 3,035 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/)
20. [**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/)
21. [**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/)
22. [**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/)
23. [**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/)
24. [**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/)
25. [**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/)
26. [**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/)
27. [**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/)
28. [**Visual Instruction Tuning**Haotian Liu, Chunyuan Li, Qingyang Wu, et al.*2023 · 2,487 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-visual-instruction-tuning/)
29. [**Direct Preference Optimization: Your Language Model is Secretly a Reward Model**Rafael Rafailov, Archit Sharma, Eric Mitchell, et al.*2023 · 2,478 citations*](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/)
30. [**Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding**Chitwan Saharia, William Chan, Saurabh Saxena, et al.*2022 · 2,416 citations*](https://md.research.elamur.ai/free/en/papers/saharia-2022-photorealistic-text-image-diffusion-models/)
31. [**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/)
32. [**Emergent Abilities of Large Language Models**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,215 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
33. [**Scaling Instruction-Finetuned Language Models**Hyung Won Chung, Le Hou, Shayne Longpre, et al.*2022 · 2,176 citations*](https://md.research.elamur.ai/free/en/papers/chung-2022-scaling-instruction-finetuned-language-models/)
34. [**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/)
35. [**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/)
36. [**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/)
37. [**Making Pre-trained Language Models Better Few-shot Learners**Tianyu Gao, Adam Fisch, Danqi Chen*2021 · 1,952 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/)
38. [**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/)
39. [**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/)
40. [**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/)
41. [**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/)
42. [**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/)
43. [**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/)
44. [**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/)
45. [**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/)
46. [**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/)
47. [**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/)
48. [**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/)
49. [**Multitask Prompted Training Enables Zero-Shot Task Generalization**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,577 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
50. [**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/)
51. [**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/)
52. [**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/)
53. [**Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision**Chao Jia, Yinfei Yang, Ye Xia, et al.*2021 · 1,528 citations*](https://md.research.elamur.ai/free/en/papers/jia-2021-scaling-up-visual-vision-language/)
54. [**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/)
55. [**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/)
56. [**GLM: General Language Model Pretraining with Autoregressive Blank Infilling**Zhengxiao Du, Yujie Qian, Xiao Liu, et al.*2022 · 1,449 citations*](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/)
57. [**DINOv2: Learning Robust Visual Features without Supervision**Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al.*2023 · 1,425 citations*](https://md.research.elamur.ai/free/en/papers/oquab-2023-dinov2-learning-robust-visual-features/)
58. [**Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?**Sewon Min, Xinxi Lyu, Ari Holtzman, et al.*2022 · 1,423 citations*](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/)
59. [**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/)
60. [**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/)
61. [**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/)
62. [**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/)
63. [**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/)
64. [**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/)
65. [**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/)
66. [**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/)
67. [**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/)
68. [**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/)
69. [**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/)
70. [**Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity**Yao Lu, Max Bartolo, Alastair Moore, et al.*2022 · 1,187 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-fantastically-ordered-prompts-where-find/)
71. [**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/)
72. [**It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners**Timo Schick, Hinrich Schütze*2021 · 1,118 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/)
73. [**Training Compute-Optimal Large Language Models**Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al.*2022 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/)
74. [**MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models**Deyao Zhu, Jun Chen, Xiaoqian Shen, et al.*2023 · 1,105 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-minigpt-4-enhancing-vision-language/)
75. [**Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them**Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al.*2023 · 1,094 citations*](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/)
76. [**Bias and Fairness in Large Language Models: A Survey**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,072 citations*](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/)
77. [**RoFormer: Enhanced Transformer with Rotary Position Embedding**Jianlin Su, Yu Lu, Shengfeng Pan, et al.*2021 · 1,072 citations*](https://md.research.elamur.ai/free/en/papers/su-2021-roformer-enhanced-transformer-rotary-position/)
78. [**InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning**Wenliang Dai, Junnan Li, Dongxu Li, et al.*2023 · 1,051 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-instructblip-towards-general-purpose-vision/)
79. [**GPT Understands, Too**Xiao Liu, Yanan Zheng, Zhengxiao Du, et al.*2021 · 1,039 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-gpt-understands-too/)
80. [**MTEB: Massive Text Embedding Benchmark**Niklas Muennighoff, Nouamane Tazi, Loïc Magne, et al.*2022 · 1,021 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-mteb-massive-text-embedding-benchmark/)
81. [**P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks**Xiao Liu, Kaixuan Ji, Yicheng Fu, et al.*2022 · 1,013 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-p-tuning-prompt-tuning-can/)
82. [**GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints**Joshua Ainslie, James Lee-Thorp, Michiel de Jong, et al.*2023 · 1,012 citations*](https://md.research.elamur.ai/free/en/papers/ainslie-2023-gqa-training-generalized-multi-query/)
83. [**A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models**Wenqi Fan; Yujuan Ding; Liangbo Ning; Shijie Wang; Hengyun Li; Dawei Yin; Tat-Seng Chua;, Qing Li*2024 · 1,006 citations*](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/)
84. [**Big Bird: Transformers for Longer Sequences**Manzil Zaheer, Guru Guruganesh, Avinava Dubey, et al.*2020 · 992 citations*](https://md.research.elamur.ai/free/en/papers/zaheer-2020-big-bird-transformers-longer-sequences/)
85. [**Solving Quantitative Reasoning Problems with Language Models**Aitor Lewkowycz, Anders Andreassen, David Dohan, et al.*2022 · 973 citations*](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/)
86. [**PaLM 2 Technical Report**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 971 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
87. [**GPQA: A Graduate-Level Google-Proof Q&A Benchmark**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/)
88. [**Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 962 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/)
89. [**Constitutional AI: Harmlessness from AI Feedback**Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.*2022 · 957 citations*](https://md.research.elamur.ai/free/en/papers/bai-2022-constitutional-ai-harmlessness-ai-feedback/)
90. [**A Survey on In-context Learning**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 954 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/)
91. [**MPNet: Masked and Permuted Pre-training for Language Understanding**Kaitao Song, Xu Tan, Tao Qin, et al.*2020 · 944 citations*](https://md.research.elamur.ai/free/en/papers/song-2020-mpnet-masked-permuted-pre-training/)
92. [**Out of One, Many: Using Language Models to Simulate Human Samples**Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, et al.*2022 · 942 citations*](https://md.research.elamur.ai/free/en/papers/argyle-2022-out-one-many-using-language/)
93. [**C-Pack: Packed Resources For General Chinese Embeddings**Shitao Xiao, Zheng Liu, Peitian Zhang, et al.*2023 · 920 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2023-c-pack-packed-resources/)
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