We’ve released new versions of GPT-3 and Codex which can edit or insert content into existing text, rather than just completing existing text.

We describe our latest thinking in the hope of helping other AI developers address safety and misuse of deployed models.

Call for expressions of interest to study the economic impacts of large language models.

Announcing GPT-NeoX-20B, a 20 billion parameter model trained in collaboration with CoreWeave.

We built a neural theorem prover for Lean that learned to solve a variety of challenging high-school olympiad problems, including problems from the AMC12 and AIME competitions, as well as two problems adapted from the IMO.

We are introducing embeddings, a new endpoint in the OpenAI API that makes it easy to perform natural language and code tasks like semantic search, clustering, topic modeling, and classification.

We’ve fine-tuned GPT-3 to more accurately answer open-ended questions using a text-based web browser.

Fine-tune with a single command.

As part of our effort to support and develop AI talent, we’re excited to announce the OpenAI Residency.

Wider availability made possible by safety progress.

We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset, while our system scored 55% on those same problems.

We perform a series of experiments using GPT-3 with decomposition to perform complex toy tasks that it is otherwise unable to solve. The goal of these experiments is to provide some preliminary evidence for the viability of factored cognition in real world models. For our synthetic task, we chose a series of various arithmetic tasks. Aside from the ease of generating examples, another advantage of arithmetic related task settings is GPT-3's inability to perform even simple mathematical operations.

There are multiple ways of evaluating multiple choice tasks on autoregressive LMs like GPT-3/Neo/J. This post lays out the current prevalent normalization methods.

Scaling human oversight of AI systems for tasks that are difficult to evaluate.

Today, we’re excited to announce the appointment of Helen Toner to our board of directors.

A comparison of Rotary Position Embedding against GPT-style learned position embeddings.