Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
We use GPT-4 to automatically write explanations for the behavior of neurons in large language models and to score those explanations. We release a dataset of these (imperfect) explanations and scores for every neuron in GPT-2.
ChatGPT users can now turn off chat history, allowing you to choose which conversations can be used to train our models.
An update on our findings, the actions we’ve taken, and technical details of the bug.
We’ve implemented initial support for plugins in ChatGPT. Plugins are tools designed specifically for language models with safety as a core principle, and help ChatGPT access up-to-date information, run computations, or use third-party services.
How Iceland is using GPT-4 to preserve its language.
We’ve created GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.
Stripe leverages GPT-4 to streamline user experience and combat fraud.
GPT-4 deepens the conversation on Duolingo.
Be My Eyes uses GPT-4 to transform visual accessibility.
Khan Academy explores the potential for GPT-4 in a limited pilot program.
It can generate, edit, and iterate with users on creative and technical writing tasks, such as composing songs, writing screenplays, or learning a user’s writing style.