InstructGPT

InstructGPT
Name	InstructGPT
Developer	OpenAI
First release	2022
Stable release	2023
Programming language	Python
Model type	Large language model
License	Proprietary

InstructGPT InstructGPT is a family of large language models developed by OpenAI intended to follow human instructions more reliably and safely than earlier generative models. It emphasizes alignment with human preferences, iterative policy refinement, and integration of reinforcement learning techniques to reduce harmful outputs while maintaining capability across diverse tasks. The project interfaces research from machine learning labs and policy teams to influence deployment strategies and public-facing products.

Overview

InstructGPT originated from research efforts at OpenAI that built on transformer architectures and reinforced learning from human feedback, drawing on precedent studies at institutions such as Google Research, DeepMind, Stanford University, MIT, Carnegie Mellon University, University of California, Berkeley, ETH Zurich, University of Toronto, University of Washington, Oxford University, Cambridge University, Harvard University, Princeton University, Yale University, Columbia University, University of California, San Diego, California Institute of Technology, Imperial College London, University of Chicago, University of Michigan, University of Edinburgh, University of Melbourne, Peking University, Tsinghua University, National University of Singapore, ETH Zurich, University of Illinois Urbana-Champaign, University of Texas at Austin, Seoul National University, University of British Columbia, University of Sydney, University of Copenhagen, Max Planck Society, Facebook AI Research, Microsoft Research, Allen Institute for AI, NVIDIA Research, Apple Machine Learning Research, IBM Research, Baidu Research, Tencent AI Lab, Samsung Research, Belfer Center for Science and International Affairs, Brookings Institution, RAND Corporation, Center for a New American Security, OpenAI Scholars.

Development and Architecture

The architecture of InstructGPT builds on transformer-based designs originally popularized by teams at Google Research (notably the authors of the original transformer paper) and extended by subsequent work at OpenAI, DeepMind, and Facebook AI Research. Engineering and infrastructure relied on cloud and HPC providers including Microsoft Azure, AWS, Google Cloud Platform, NVIDIA hardware, and collaborations with research groups at Stanford University and MIT. Model scaling traces lineage to milestones associated with models from OpenAI, Google DeepMind's AlphaFold team, Anthropic, Cohere, EleutherAI, Hugging Face, AI21 Labs, Baidu, Alibaba DAMO Academy, ByteDance AI Lab, Salesforce Research, Uber ATG, DeepMind's Gopher, Meta AI Research, OpenAI Codex, OpenAI GPT-3, Transformer-XL, BART, T5, RoBERTa, XLNet, ALBERT, ERNIE, DeBERTa, Switch Transformer, Swin Transformer. System design incorporated lessons from projects at Lawrence Berkeley National Laboratory and Argonne National Laboratory for efficient parallelism and distributed training.

Training Methodology

Training methodology combined supervised fine-tuning and reinforcement learning from human feedback (RLHF) informed by practices and critiques from Stanford's Human-Centered AI, Harvard Kennedy School, Oxford's Future of Humanity Institute, Center for Security and Emerging Technology, AI Now Institute, Partnership on AI, Electronic Frontier Foundation, Mozilla Foundation, Access Now, AlgorithmWatch, Data & Society Research Institute, Ada Lovelace Institute, Leverhulme Centre for the Future of Intelligence, Alan Turing Institute, Institute for Human-Centered AI at Stanford. The approach used human labelers drawn from contractors or research assistants and relied on annotation guidelines influenced by ethics teams at OpenAI and external consultants from Carnegie Mellon University, University of Oxford, and University College London. Optimization techniques referenced research from Yoshua Bengio-related groups, Geoffrey Hinton's work, and methods explored at DeepMind and Google Brain.

Safety, Alignment, and Ethical Considerations

Safety and alignment efforts were informed by cross-institutional discourse including inputs from Future of Life Institute, Center for AI Safety, Lawrence Livermore National Laboratory, Brookings Institution, RAND Corporation, Electronic Frontier Foundation, Human Rights Watch, Amnesty International, UNESCO, World Health Organization, European Commission, European Parliament, UK Information Commissioner's Office, US Federal Trade Commission, US Department of Commerce, AI Now Institute, Partnership on AI, Center for Democracy & Technology, OpenAI's policy team, Harvard Berkman Klein Center, Stanford Internet Observatory, Oxford Internet Institute, Mozilla Foundation, Ada Lovelace Institute, Data & Society Research Institute, Transparency International. Considerations included content filtering, red-team adversarial testing informed by teams at Google Project Zero and Microsoft Threat Intelligence, and policy deliberations relevant to export controls and disclosure norms discussed at United Nations forums and national bodies.

Evaluation and Performance

Evaluation used benchmarks and human evaluations derived from work at GLUE benchmark creators, groups at Stanford, NYU, University of Washington, Allen Institute for AI, Hugging Face, Microsoft Research, Google Research, DeepMind, Carnegie Mellon University, SRI International, OpenAI, Anthropic, Cohere, EleutherAI, AI2, MIT CSAIL, Tsinghua University, Peking University, NVIDIA Research, Facebook AI Research, IBM Research, Salesforce Research, Uber ATG. Reported metrics emphasized improvements in human-preference alignment, reductions in toxic outputs relative to earlier baselines, and task performance on language understanding and generation tasks comparable to contemporaneous models from Google DeepMind and Meta AI Research.

Applications and Use Cases

InstructGPT was positioned for deployment across interfaces and products influenced by commercial and academic adopters including Microsoft, GitHub Copilot collaborations, Stripe, Coursera, Duolingo, Khan Academy, Grammarly, Slack Technologies, Notion Labs, Salesforce, Zendesk, ServiceNow, Amazon Web Services, Google Cloud Platform, IBM Watson, SAP, Accenture, Deloitte, McKinsey & Company, Bain & Company, Boston Consulting Group, Reuters, The New York Times, The Washington Post, BBC, Bloomberg, Associated Press, Wolfram Research, MathWorks, Zebra Medical Vision, Siemens Healthineers, Philips Healthcare, Mayo Clinic, Cleveland Clinic, Johns Hopkins Medicine, Mount Sinai Health System, Kaiser Permanente.

Criticisms and Controversies

Critiques and controversies involved labor practices, dataset provenance, and deployment risks debated by groups such as Electronic Frontier Foundation, ACLU, Human Rights Watch, Amnesty International, Center for AI Safety, Future of Life Institute, AI Now Institute, Transparency International, OpenAI Critics, Academic Freedom advocates, US Congress committees, European Parliament hearings, UK Parliament discussions, French National Assembly consultations, German Bundestag panels, and investigative reporting by outlets like The New York Times, The Washington Post, The Guardian, Financial Times, Wall Street Journal, Wired, MIT Technology Review, Bloomberg. Debates focused on transparency, reproducibility, labor conditions for annotators, monetization, and comparative advantage relative to research from Anthropic, DeepMind, Google Research, Meta AI Research, Microsoft Research, Stanford Human-Centered AI, Berkeley AI Research.

Category:Artificial intelligence systems