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For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. 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These InstructGPT models, which are trained with humans in the loop, are now deployed as the default language models on our API.","id":"3THcUXwKBaM0SYvu8zjvTk","__typename":"page","updatedAt":"2024-04-25T23:06:34.768Z","createdAt":"2024-03-27T02:55:42.970Z"},"external":false,"ratio":"3:4","useScrim":true,"scrimColor":"0000FF","textStyle":"light","style":"Media","id":"nK4DWy52bIru49gWkgqKo","__typename":"card","updatedAt":"2024-04-18T18:21:38.302Z","createdAt":"2024-04-14T01:13:12.687Z"},{"headline":"We've trained a model to summarize entire books with human feedback.","eyebrow":"Summarizing books with human feedback","hideDate":true,"page":{"slug":"index/summarizing-books","category":{"name":"Safety & Alignment","slug":"safety-and-alignment","includeInNewsIndex":true,"id":"1tX5z7w84djMXytguWjfxj","__typename":"category","updatedAt":"2024-06-13T21:00:04.044Z","createdAt":"2024-04-25T18:26:09.510Z"},"title":"Summarizing books with human feedback","publicationDate":"2021-09-23T07:00:00.000Z","publicationDateText":"September 23, 2021","coverImage":{"title":"Summarizing Books","description":"","file":{"url":"//images.ctfassets.net/kftzwdyauwt9/5a1f46b8-7e89-42f1-cb71b084ba93/524e90bcc1dfde4121a5ffb83c679bc9/summarizing-books.jpg","details":{"size":780273,"image":{"width":3441,"height":1936}},"fileName":"summarizing-books.jpg","contentType":"image/jpeg"},"updatedAt":"2024-03-25T14:54:03.946Z","createdAt":"2024-03-25T14:54:03.946Z"},"coverImageAlt":"Five overlapping book covers cascading from the top left to the bottom right of the frame.","coverImageScrimColor":"000000","pageType":"Article","showTableOfContents":true,"showReadAloud":true,"omitFromListings":false,"searchText":"Summarizing books with human feedback\n\nScaling human oversight of AI systems for tasks that are difficult to evaluate.","id":"4cSnQmpaUx1UfvRXazVLRr","__typename":"page","updatedAt":"2024-04-25T23:06:29.754Z","createdAt":"2024-03-27T02:56:37.805Z"},"external":false,"ratio":"3:4","useScrim":false,"scrimColor":"354263","style":"Media","id":"3hjo653XdXOE3rbG8WhI7y","__typename":"card","updatedAt":"2024-04-18T18:22:14.032Z","createdAt":"2024-04-14T01:14:15.910Z"},{"headline":"We trained GPT-3, an autoregressive language model with 175 billion parameters.","eyebrow":"Language models are few-shot learners","hideDate":true,"page":{"slug":"index/language-models-are-few-shot-learners","category":{"name":"Research","slug":"research","includeInNewsIndex":true,"id":"5wGbg5aYZ4v8Ms970UzsnM","__typename":"category","updatedAt":"2024-05-23T21:47:23.289Z","createdAt":"2024-03-25T14:00:08.172Z"},"title":"Language models are few-shot learners","publicationDate":"2020-05-28T07:00:00.000Z","publicationDateText":"May 28, 2020","coverImage":{"title":"Language Models Are Few Shot Learners","description":"","file":{"url":"//images.ctfassets.net/kftzwdyauwt9/40660b0a-561d-412e-9adc992b9f69/9c841f18126a6af1e3b3d266988ab0e8/image_126.png","details":{"size":3810450,"image":{"width":2048,"height":2048}},"fileName":"image 126.png","contentType":"image/png"},"updatedAt":"2024-04-24T18:04:38.985Z","createdAt":"2024-03-25T14:48:05.214Z"},"coverImageAlt":"An abstract expressionist painting in a soft palette depicting a blue body of water and a vibrant orange sky.","coverImageScrimColor":"000000","pageType":"Article","showTableOfContents":true,"showReadAloud":true,"omitFromListings":false,"searchText":"$1a","id":"77R96knKe95dNuvkT9EZ7H","__typename":"page","updatedAt":"2024-04-25T23:08:54.754Z","createdAt":"2024-03-27T03:01:13.866Z"},"ratio":"3:4","useScrim":true,"scrimColor":"354263","textStyle":"light","style":"Media","id":"1ORVf5fmvVSEBEnodCFiJz","__typename":"card","updatedAt":"2024-04-18T18:22:26.354Z","createdAt":"2024-04-14T01:15:29.437Z"}],"headerAlignment":"Center","__typename":"componentCardCarousel","updatedAt":"2024-04-14T01:22:07.228Z","createdAt":"2024-04-14T01:15:31.442Z"}]
1b:T446,Hierarchical text-conditional image generation with CLIP latents

Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. Moreover, the joint embedding space of CLIP enables language-guided image manipulations in a zero-shot fashion. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples.12:["$","$L19",null,{"id":"4B9llmaMxW3JNNpwfyBJqf","headline":"Image","headlineSize":"Heading 4","description":"Our research on generative modeling for images has led to representation models like CLIP, which makes a map between text and images that an AI can read, and DALL-E, a tool for creating vivid images from text descriptions.","cards":[{"headline":"We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity.","eyebrow":"Hierarchical text-conditional image generation with CLIP 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natural language.","id":"2LtHNeCqOQwpaoibhxixSJ","__typename":"page","updatedAt":"2024-04-25T23:21:24.834Z","createdAt":"2024-03-27T02:59:40.226Z"},"ratio":"3:4","useScrim":true,"scrimColor":"000000","style":"Media","id":"42wBKYAiOxfwyieO5buXw6","__typename":"card","updatedAt":"2024-04-18T18:23:08.420Z","createdAt":"2024-04-14T01:52:23.870Z"},{"headline":"We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision.","eyebrow":"CLIP: Connecting text and images","hideDate":true,"page":{"slug":"index/clip","category":{"name":"Research","slug":"research","includeInNewsIndex":true,"id":"5wGbg5aYZ4v8Ms970UzsnM","__typename":"category","updatedAt":"2024-05-23T21:47:23.289Z","createdAt":"2024-03-25T14:00:08.172Z"},"title":"CLIP: Connecting text and images","publicationDate":"2021-01-05T08:00:00.000Z","publicationDateText":"January 5, 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13:["$","$L19",null,{"id":"7e2MHKa7wpq49MIzR6qRq1","headline":"Audio","headlineSize":"Heading 4","description":"Our research on applying AI to audio processing and audio generation has led to developments in automatic speech recognition and original musical compositions.","cards":[{"hideDate":true,"page":{"slug":"index/navigating-the-challenges-and-opportunities-of-synthetic-voices","category":{"name":"Product","slug":"product","includeInNewsIndex":true,"id":"a29K9BX4fH1dKiS52nzFa","__typename":"category","updatedAt":"2024-06-13T01:59:38.523Z","createdAt":"2024-03-26T23:19:06.007Z"},"title":"Navigating the challenges and opportunities of synthetic voices","publicationDate":"2024-03-29","publicationDateText":"March 29, 2024","coverImage":{"title":" Navigating the challenges > Media item","description":"","file":{"url":"//images.ctfassets.net/kftzwdyauwt9/3u7r7E3gFRGZIxdKucf7f6/fea2b25e91c1f53a0e3f839a18c1caae/Navigating_the_Challenges_and_Opportunities_of_Synthetic_Voices.jpg","details":{"size":90113,"image":{"width":1920,"height":1920}},"fileName":"Navigating the Challenges and Opportunities of Synthetic Voices.jpg","contentType":"image/jpeg"},"updatedAt":"2024-05-01T23:55:42.694Z","createdAt":"2024-04-17T23:09:22.869Z"},"coverImageAlt":"Soft chalk pastels mimicking a dawn sky.","coverImageScrimColor":"000000","pageType":"Article","showTableOfContents":true,"showReadAloud":true,"omitFromListings":false,"searchText":"Navigating the Challenges and Opportunities of Synthetic Voices\n\nWe’re sharing lessons from a small scale preview of Voice Engine, a model for creating custom voices.","id":"5Dx4Plxz425PrHAaqvpb6S","__typename":"page","updatedAt":"2024-04-26T01:17:32.373Z","createdAt":"2024-03-27T03:58:43.834Z"},"external":false,"ratio":"4:3","useScrim":false,"scrimColor":"000000","textStyle":"dark","style":"Media","id":"36PcllHSI13bFuwMDcFCud","__typename":"card","updatedAt":"2024-04-26T14:52:09.118Z","createdAt":"2024-04-24T19:10:31.042Z"},{"headline":"We’ve trained and are open-sourcing a neural net that approaches human level robustness and accuracy on English speech recognition.","eyebrow":"Introducing Whisper","hideDate":true,"page":{"slug":"index/whisper","category":{"name":"Research","slug":"research","includeInNewsIndex":true,"id":"5wGbg5aYZ4v8Ms970UzsnM","__typename":"category","updatedAt":"2024-05-23T21:47:23.289Z","createdAt":"2024-03-25T14:00:08.172Z"},"title":"Introducing Whisper","publicationDate":"2022-09-21T07:00:00.000Z","publicationDateText":"September 21, 2022","coverImage":{"title":"Whisper","description":"","file":{"url":"//images.ctfassets.net/kftzwdyauwt9/13c810cb-0592-442d-190ab7378bef/4a037973aae58fb9c608185d28866c7d/Frame_5.png","details":{"size":2901883,"image":{"width":1286,"height":1286}},"fileName":"Frame 5.png","contentType":"image/png"},"updatedAt":"2024-04-24T17:47:53.805Z","createdAt":"2024-03-25T14:49:40.875Z"},"coverImageAlt":"An abstract expressionist painting with soft, pastel colors depicting a pond and orange flowers.","coverImageScrimColor":"000000","pageType":"Article","showTableOfContents":true,"showReadAloud":true,"omitFromListings":false,"searchText":"Introducing Whisper\n\nWe’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.","id":"2HYzGbrAMIfAgO0OGrhT8r","__typename":"page","updatedAt":"2024-04-25T23:09:19.858Z","createdAt":"2024-03-27T02:52:25.814Z"},"external":false,"ratio":"4:3","useScrim":true,"scrimColor":"354263","style":"Media","id":"5tpRKtzw0xGf013CK8TQpo","__typename":"card","updatedAt":"2024-04-26T14:52:17.058Z","createdAt":"2024-04-14T02:00:45.903Z"}],"headerAlignment":"Center","__typename":"componentCardCarousel","updatedAt":"2024-04-26T01:44:34.620Z","createdAt":"2024-04-14T02:02:34.805Z"}]
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By establishing a correlation between sample quality and image classification accuracy, we show that our best generative model also contains features competitive with top convolutional nets in the unsupervised setting.","id":"1bdTXsKIHBvceA7QEQ8PFM","__typename":"page","updatedAt":"2024-04-25T23:21:19.686Z","createdAt":"2024-03-27T03:01:06.200Z"},"external":false,"ratio":"3:4","useScrim":true,"scrimColor":"354263","style":"Media","id":"28Zb1XtsbCHAaTRaoeIgne","__typename":"card","updatedAt":"2024-04-18T18:24:48.302Z","createdAt":"2024-04-14T02:10:17.706Z"},{"headline":"Solving Rubik’s Cube with a robot hand","hideDate":false,"page":{"slug":"index/solving-rubiks-cube","category":{"name":"Research","slug":"research","includeInNewsIndex":true,"id":"5wGbg5aYZ4v8Ms970UzsnM","__typename":"category","updatedAt":"2024-05-23T21:47:23.289Z","createdAt":"2024-03-25T14:00:08.172Z"},"title":"Solving Rubik’s Cube with a robot hand","publicationDate":"2019-10-15T07:00:00.000Z","publicationDateText":"October 15, 2019","coverImage":{"title":"Outstretched robot arm solving a Rubik's cube in its palm in front of a cloudy purple background","description":"","file":{"url":"//images.ctfassets.net/kftzwdyauwt9/9267c72e-6cc1-42f6-d23abb5fa261/3bb199192cd8749d8adc83a71751833c/solving-rubiks-cube.jpg","details":{"size":128660,"image":{"width":1920,"height":1080}},"fileName":"solving-rubiks-cube.jpg","contentType":"image/jpeg"},"updatedAt":"2024-03-25T14:39:55.460Z","createdAt":"2024-03-25T14:39:55.460Z"},"coverImageAlt":"A Rubik's Cube in a robots hands against a sky background.","coverImageScrimColor":"000000","pageType":"Article","showTableOfContents":true,"showReadAloud":true,"omitFromListings":false,"searchText":"Solving Rubik’s Cube with a robot hand\n\nWe’ve trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. 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