Learning transferable visual models from natural language supervision A Radford, JW Kim, C Hallacy, A Ramesh, G Goh, S Agarwal, G Sastry, ... International conference on machine learning, 8748-8763, 2021 | 24821 | 2021 |
Training language models to follow instructions with human feedback L Ouyang, J Wu, X Jiang, D Almeida, C Wainwright, P Mishkin, C Zhang, ... Advances in neural information processing systems 35, 27730-27744, 2022 | 10302 | 2022 |
Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 5727 | 2023 |
Evaluating large language models trained on code M Chen, J Tworek, H Jun, Q Yuan, HPDO Pinto, J Kaplan, H Edwards, ... arXiv preprint arXiv:2107.03374, 2021 | 3310 | 2021 |
Glide: Towards photorealistic image generation and editing with text-guided diffusion models A Nichol, P Dhariwal, A Ramesh, P Shyam, P Mishkin, B McGrew, ... arXiv preprint arXiv:2112.10741, 2021 | 3105 | 2021 |
Gpts are gpts: An early look at the labor market impact potential of large language models T Eloundou, S Manning, P Mishkin, D Rock arXiv preprint arXiv:2303.10130, 2023 | 870 | 2023 |
Point-e: A system for generating 3d point clouds from complex prompts A Nichol, H Jun, P Dhariwal, P Mishkin, M Chen arXiv preprint arXiv:2212.08751, 2022 | 441 | 2022 |
Training language models to follow instructions with human feedback, 2022 L Ouyang, J Wu, X Jiang, D Almeida, CL Wainwright, P Mishkin, C Zhang, ... URL https://arxiv. org/abs/2203.02155 13, 1, 2022 | 254 | 2022 |
Training language models to follow instructions with human feedback. arXiv L Ouyang, J Wu, X Jiang, D Almeida, CL Wainwright, P Mishkin, C Zhang, ... arXiv preprint arXiv:2203.02155, 2022 | 116 | 2022 |
DALL· E: Creating images from text A Ramesh, M Pavlov, G Goh, S Gray, M Chen, R Child, V Misra, P Mishkin, ... OpenAI blog 2, 2021 | 100 | 2021 |
Learning transferable visual models from natural language supervision. arXiv A Radford, JW Kim, C Hallacy, A Ramesh, G Goh, S Agarwal, G Sastry, ... arXiv preprint arXiv:2103.00020, 2021 | 92 | 2021 |
International conference on machine learning A Radford, JW Kim, C Hallacy, A Ramesh, G Goh, S Agarwal, G Sastry, ... PMLR, 2021 | 58 | 2021 |
Learning Transferable Visual Models From Natural Language Supervision, Feb. 2021 A Radford, JW Kim, C Hallacy, A Ramesh, G Goh, S Agarwal, G Sastry, ... URL http://arxiv. org/abs/2103.00020, 0 | 57 | |
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models.” arXiv T Eloundou, S Manning, P Mishkin, D Rock arXiv preprint arXiv:2303.10130, 2023 | 56 | 2023 |
Evaluating large language models trained on code. arXiv 2021 M Chen, J Tworek, H Jun, Q Yuan, HPO Pinto, J Kaplan, H Edwards, ... arXiv preprint arXiv:2107.03374 10, 2021 | 55 | 2021 |
Training language models to follow instructions with human feedback, March 2022 L Ouyang, J Wu, X Jiang, D Almeida, CL Wainwright, P Mishkin, C Zhang, ... URL http://arxiv. org/abs/2203.02155 92, 0 | 51 | |
DALL·E 2 Preview - Risks and Limitations P Mishkin, L Ahmad, M Brundage, G Krueger, G Sastry https://github.com/openai/dalle-2-preview/blob/main/system-card_04062022.md, 2022 | 43 | 2022 |
Practices for governing agentic AI systems Y Shavit, S Agarwal, M Brundage, S Adler, C O’Keefe, R Campbell, T Lee, ... Research Paper, OpenAI, December, 2023 | 34 | 2023 |
GPTs are GPTs: Labor market impact potential of LLMs T Eloundou, S Manning, P Mishkin, D Rock Science 384 (6702), 1306-1308, 2024 | 30 | 2024 |
A hazard analysis framework for code synthesis large language models H Khlaaf, P Mishkin, J Achiam, G Krueger, M Brundage arXiv preprint arXiv:2207.14157, 2022 | 22 | 2022 |