GPT-3: The Moment Few-Shot Prompting Became the Interface
GPT-3 showed that a 175B autoregressive language model could perform many tasks from examples in the prompt, without gradient updates or task-specific fine-tuning.
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OpenAI
An AI research and deployment company behind GPT, CLIP, DALL·E, and other frontier systems.
InstructGPT: Why Bigger Models Still Needed Human Feedback
InstructGPT showed that human preference data and RLHF could make smaller models more helpful and aligned than much larger raw language models.
Whisper: Speech Recognition Trained on Web-Scale Weak Supervision
Whisper showed that large, diverse, weakly supervised audio data can produce robust multilingual speech recognition and translation models.
DALL·E 2: Text-to-Image Generation Through CLIP Latents
DALL·E 2 splits text-to-image generation into a prior that predicts a CLIP image embedding and a decoder that turns that embedding into an image.
CLIP: Computer Vision Learns to Read Natural Language
CLIP trains image and text encoders on 400 million internet image-text pairs, making natural language a flexible interface for zero-shot visual recognition.
GPT-4: The Report That Made Frontier Models Feel Measurable
GPT-4 was less a full recipe than a measurement document: a multimodal Transformer whose benchmark performance, scaling predictability, and post-training alignment reset expectations for frontier AI.