---
title: "Who cites Jack W. Rae — 13,732 papers of our library"
description: "13,732 papers of the Elamur Research library cite the works of Jack W. Rae, found in their bibliographies · page 1 of 31 · 1–100; the most cited first."
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# Who cites Jack W. Rae

17,168 citations of their works from 13,732 papers of our library (of the 149,439 whose bibliographies we read) · 3,050 with a page here, the most cited first · page 1 of 31 · 1–100 · [← Jack W. Rae](https://md.research.elamur.ai/free/en/authors/jack-w-rae/)

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1. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
2. [**Chain-of-Thought Prompting Elicits Reasoning in Large Language Models arXiv:2201.11903**Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.*2022 · 11,499 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
3. [**Training language models to follow instructions with human feedback arXiv:2203.02155**Long Ouyang, Jeff Wu, Xu Jiang, et al.*2022 · 10,224 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
4. [**Llama 2: Open Foundation and Fine-Tuned Chat Models arXiv:2307.09288**Hugo Touvron, Louis Martin, Kevin Stone, et al.*2023 · 9,669 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/)
5. [**LLaMA: Open and Efficient Foundation Language Models arXiv:2302.13971**Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al.*2023 · 9,603 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/)
6. [**The Llama 3 Herd of Models arXiv:2407.21783**Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.*2024 · 7,682 citations*](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
7. **[Survey of Hallucination in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,974 citations*
8. [**PaLM: Scaling Language Modeling with Pathways arXiv:2204.02311**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
9. **[DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**DeepSeek-AI, Daya Guo, Dejian Yang, et al.*2025 · 4,728 citations*
10. **[Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://md.research.elamur.ai/free/en/papers/zheng-2023-judging-llm-judge-mt-bench/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al.*2023 · 4,573 citations*
11. [**Longformer: The Long-Document Transformer arXiv:2004.05150**Iz Beltagy, Matthew E. Peters, Arman Cohan*2020 · 3,880 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2020-longformer-long-document-transformer/)
12. **[Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.*2022 · 3,832 citations*
13. [**Categorical Reparameterization with Gumbel-Softmax arXiv:1611.01144**Eric Jang, Shixiang Gu, Ben Poole*2016 · 3,656 citations*](https://md.research.elamur.ai/free/en/papers/jang-2016-categorical-reparameterization-gumbel-softmax/)
14. **[A Survey on Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,648 citations*
15. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
16. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
17. [**Self-Consistency Improves Chain of Thought Reasoning in Language Models arXiv:2203.11171**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 3,121 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/)
18. [**Gemini: A Family of Highly Capable Multimodal Models arXiv:2312.11805**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 3,050 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
19. **[QLoRA: Efficient Finetuning of Quantized LLMs](https://md.research.elamur.ai/free/en/papers/dettmers-2023-qlora-efficient-finetuning-quantized-llms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, et al.*2023 · 2,929 citations*
20. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
21. [**Visual Instruction Tuning arXiv:2304.08485**Haotian Liu, Chunyuan Li, Qingyang Wu, et al.*2023 · 2,617 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-visual-instruction-tuning/)
22. [**Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding arXiv:2205.11487**Chitwan Saharia, William Chan, Saurabh Saxena, et al.*2022 · 2,533 citations*](https://md.research.elamur.ai/free/en/papers/saharia-2022-photorealistic-text-image-diffusion-models/)
23. [**Sparks of Artificial General Intelligence: Early experiments with GPT-4 arXiv:2303.12712**Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.*2023 · 2,439 citations*](https://md.research.elamur.ai/free/en/papers/bubeck-2023-sparks-artificial-general-intelligence-early/)
24. [**GPT-4o System Card arXiv:2410.21276**OpenAI, Aaron Hurst, Adam Lerer, et al.*2024 · 2,304 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-gpt-4o-system-card/)
25. **[Reflexion: Language Agents with Verbal Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/shinn-2023-reflexion-language-agents-verbal-reinforcement/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Noah Shinn, Federico Cassano, Edward Berman, et al.*2023 · 2,271 citations*
26. [**Emergent Abilities of Large Language Models arXiv:2206.07682**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,269 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
27. [**Flamingo: a Visual Language Model for Few-Shot Learning arXiv:2204.14198**Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, et al.*2022 · 2,246 citations*](https://md.research.elamur.ai/free/en/papers/alayrac-2022-flamingo-visual-language-model-few/)
28. [**BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models arXiv:2301.12597**Junnan Li, Dongxu Li, Silvio Savarese, et al.*2023 · 2,219 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-blip-2-bootstrapping-language-image/)
29. [**DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models arXiv:2402.03300**Zhihong Shao, Peiyi Wang, Qihao Zhu, et al.*2024 · 2,115 citations*](https://md.research.elamur.ai/free/en/papers/shao-2024-deepseekmath-pushing-limits-mathematical-reasoning/)
30. **[TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Stephanie Lin, Jacob Hilton, Owain Evans*2022 · 2,088 citations*
31. **[Toolformer: Language Models Can Teach Themselves to Use Tools](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, et al.*2023 · 2,065 citations*
32. **[Training Compute-Optimal Large Language Models](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al.*2022 · 2,032 citations*
33. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
34. [**OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068**Susan Zhang, Stephen Roller, Naman Goyal, et al.*2022 · 1,755 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
35. **[Language (Technology) is Power: A Critical Survey of “Bias” in NLP](https://md.research.elamur.ai/free/en/papers/blodgett-2020-language-technology-is-power-critical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Su Lin Blodgett, Solon Barocas, Hal Daumé III, et al.*2020 · 1,716 citations*
36. [**Qwen Technical Report arXiv:2309.16609**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,669 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
37. [**Retrieval-Augmented Generation for Large Language Models: A Survey arXiv:2312.10997**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,663 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
38. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
39. **[Competition-Level Code Generation with AlphaCode](https://md.research.elamur.ai/free/en/papers/li-2022-competition-level-code-generation-alphacode/) [Science](https://md.research.elamur.ai/free/en/venues/science/)**Yujia Li, David Choi, Junyoung Chung, et al.*2022 · 1,564 citations*
40. **[Efficient Transformers: A Survey](https://md.research.elamur.ai/free/en/papers/tay-2020-efficient-transformers-survey/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Yi Tay, Mostafa Dehghani, Dara Bahri, et al.*2020 · 1,539 citations*
41. [**DINOv2: Learning Robust Visual Features without Supervision arXiv:2304.07193**Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al.*2023 · 1,513 citations*](https://md.research.elamur.ai/free/en/papers/oquab-2023-dinov2-learning-robust-visual-features/)
42. [**Code Llama: Open Foundation Models for Code arXiv:2308.12950**Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, et al.*2024 · 1,430 citations*](https://md.research.elamur.ai/free/en/papers/roziere-2024-code-llama-open-foundation-models/)
43. [**Let's Verify Step by Step arXiv:2305.20050**Hunter Lightman, Vineet Kosaraju, Yura Burda, et al.*2023 · 1,412 citations*](https://md.research.elamur.ai/free/en/papers/lightman-2023-let-s-verify-step-step/)
44. [**LaMDA: Language Models for Dialog Applications arXiv:2201.08239**Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al.*2022 · 1,344 citations*](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/)
45. **[Parameter-efficient fine-tuning of large-scale pre-trained language models](https://md.research.elamur.ai/free/en/papers/ding-2023-parameter-efficient-fine-tuning-large/) [Nature Machine Intelligence](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/)**Ning Ding, Yujia Qin, Guang You Yang, et al.*2023 · 1,325 citations*
46. **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,287 citations*
47. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations*
48. [**FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning arXiv:2307.08691**Tri Dao*2023 · 1,240 citations*](https://md.research.elamur.ai/free/en/papers/dao-2023-flashattention-2-faster-attention-better/)
49. **[Recipes for Building an Open-Domain Chatbot](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Stephen Roller, Emily Dinan, Naman Goyal, et al.*2021 · 1,214 citations*
50. [**A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly arXiv:2312.02003**Yifan Yao, Jinhao Duan, Kaidi Xu, et al.*2023 · 1,186 citations*](https://md.research.elamur.ai/free/en/papers/yao-2023-survey-large-language-model-llm/)
51. [**MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models arXiv:2304.10592**Deyao Zhu, Jun Chen, Xiaoqian Shen, et al.*2023 · 1,141 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-minigpt-4-enhancing-vision-language/)
52. [**Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone arXiv:2404.14219**Marah Abdin, Jyoti Aneja, Hany Awadalla, et al.*2024 · 1,134 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/)
53. **[Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al.*2023 · 1,119 citations*
54. [**OpenAI o1 System Card arXiv:2412.16720**OpenAI, Aaron Jaech, Adam Kalai, et al.*2024 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-openai-o1-system-card/)
55. [**InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning arXiv:2305.06500**Wenliang Dai, Junnan Li, Dongxu Li, et al.*2023 · 1,100 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-instructblip-towards-general-purpose-vision/)
56. **[Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,082 citations*
57. **[MTEB: Massive Text Embedding Benchmark](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-mteb-massive-text-embedding-benchmark/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Niklas Muennighoff, Nouamane Tazi, Loïc Magne, et al.*2022 · 1,032 citations*
58. [**Qwen2.5-VL Technical Report arXiv:2502.13923**Shuai Bai, Keqin Chen, Xuejing Liu, et al.*2025 · 1,031 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
59. **[Big Bird: Transformers for Longer Sequences](https://md.research.elamur.ai/free/en/papers/zaheer-2020-big-bird-transformers-longer-sequences/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Manzil Zaheer, Guru Guruganesh, Avinava Dubey, et al.*2020 · 1,023 citations*
60. [**GPQA: A Graduate-Level Google-Proof Q&A Benchmark arXiv:2311.12022**David Rein, Betty Li Hou, Asa Cooper Stickland, et al.*2023 · 1,018 citations*](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/)
61. **[A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Wenqi Fan; Yujuan Ding; Liangbo Ning; Shijie Wang; Hengyun Li; Dawei Yin; Tat-Seng Chua;, Qing Li*2024 · 1,010 citations*
62. **[MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://md.research.elamur.ai/free/en/papers/yue-2023-mmmu-massive-multi-discipline-multimodal/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Xiang Yue, Yuansheng Ni, Kai Zhang, et al.*2023 · 1,005 citations*
63. **[Evaluating Object Hallucination in Large Vision-Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-evaluating-object-hallucination-large-vision/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yifan Li, Yifan Du, Kun Zhou, et al.*2023 · 1,004 citations*
64. [**PaLM 2 Technical Report arXiv:2305.10403**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 1,000 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
65. **[Solving Quantitative Reasoning Problems with Language Models](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Aitor Lewkowycz, Anders Andreassen, David Dohan, et al.*2022 · 992 citations*
66. **[FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sewon Min, Kalpesh Krishna, Xinxi Lyu, et al.*2023 · 987 citations*
67. [**A Survey on In-context Learning arXiv:2301.00234**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 978 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/)
68. **[Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 977 citations*
69. [**Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities arXiv:2507.06261**Gheorghe Comanici, Eric Bieber, Mike Schaekermann, et al.*2025 · 974 citations*](https://md.research.elamur.ai/free/en/papers/comanici-2025-gemini-2-5-pushing-frontier/)
70. [**Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond arXiv:2308.12966**Jinze Bai, Shuai Bai, Shusheng Yang, et al.*2023 · 970 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/)
71. [**Segment Anything arXiv:2304.02643**Alexander Kirillov, Eric Mintun, Nikhila Ravi, et al.*2023 · 960 citations*](https://md.research.elamur.ai/free/en/papers/kirillov-2023-segment-anything/)
72. **[MMBench: Is Your Multi-modal Model an All-around Player?](https://md.research.elamur.ai/free/en/papers/liu-2023-mmbench-multi-modal-model-all/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Yuan Liu, Haodong Duan, Yuanhan Zhang, et al.*2023 · 942 citations*
73. [**Do As I Can, Not As I Say: Grounding Language in Robotic Affordances arXiv:2204.01691**Michael Ahn, Anthony Brohan, Noah Brown, et al.*2022 · 941 citations*](https://md.research.elamur.ai/free/en/papers/ahn-2022-i-i-say-grounding-language/)
74. [**Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models arXiv:2304.01852 · Meta-Radiology (2023)100,017**Yiheng Liu, Tianle Han, Siyuan Ma, et al.*2023 · 940 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-summary-chatgpt-related-research-perspective/)
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76. **[CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://md.research.elamur.ai/free/en/papers/nangia-2020-crows-pairs-challenge-dataset-measuring/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nikita Nangia, Clara Vania, Rasika Bhalerao, et al.*2020 · 897 citations*
77. [**A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity arXiv:2302.04023**Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, et al.*2023 · 882 citations*](https://md.research.elamur.ai/free/en/papers/bang-2023-multitask-multilingual-multimodal-evaluation-chatgpt/)
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79. [**ChatGPT: Jack of all trades, master of none arXiv:2302.10724 · Information Fusion 101,861 (2023)**Jan Kocon, Igor Cichecki, Oliwier Kaszyca, et al.*2023 · 866 citations*](https://md.research.elamur.ai/free/en/papers/kocon-2023-chatgpt-jack-all-trades-master/)
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82. [**Reformer: The Efficient Transformer arXiv:2001.04451**Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya*2020 · 829 citations*](https://md.research.elamur.ai/free/en/papers/kitaev-2020-reformer-efficient-transformer/)
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