Evaluating large language models trained on code
Update on May 16, 2025: We launched Codex, a cloud-based software engineering agent that can work on many tasks in parallel. Learn more.
Concept
Update on May 16, 2025: We launched Codex, a cloud-based software engineering agent that can work on many tasks in parallel. Learn more.
We’re proud to announce that the 2021 class of OpenAI Scholars has completed our six-month mentorship program and have produced an open-source research project with stipends and support from OpenAI.
OpenAI is committed to developing general-purpose artificial intelligence that benefits all humanity, and we believe that achieving our goal requires expertise in public policy as well as technology. So, we’re delighted to announce that Congressman Will...
Over 300 applications are delivering GPT-3–powered search, conversation, text completion, and other advanced AI features through our API.
We’ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIP’s accuracy in classifying surprising visual renditions of concepts, and is also an important step toward...
On October 14th, 2020, researchers from OpenAI, the Stanford Institute for Human-Centered Artificial Intelligence, and other universities convened to discuss open research questions surrounding GPT‑3, the largest publicly-disclosed dense language model at...
We’ve scaled Kubernetes clusters to 7,500 nodes, producing a scalable infrastructure for large models like GPT-3, CLIP, and DALL·E, but also for rapid small-scale iterative research such as Scaling Laws for Neural Language Models.
We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized,...
It’s been a year of dramatic change and growth at OpenAI.
OpenAI has agreed to license GPT-3 to Microsoft for their own products and services.
We explore the application of transformer-based language models to automated theorem proving. This work is motivated by the possibility that a major limitation of automated theorem provers compared to humans -- the generation of original mathematical terms...
We’ve applied reinforcement learning from human feedback to train language models that are better at summarization.