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A prompt for a text-to-text language model can be a query, a command, or a longer statement including context, instructions, and conversation history. Prompt engineering may involve phrasing a query, specifying a style, choice of words and grammar, [ 3 ] providing relevant context, or describing a character for the AI to mimic.
Images are paired with text prompts that contain the name of the class the subject belongs to, plus a unique identifier. As an example, a photograph of a [Nissan R34 GTR] car , with car being the class); a class-specific prior preservation loss is applied to encourage the model to generate diverse instances of the subject based on what the ...
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DALL-E, DALL-E 2, and DALL-E 3 (stylised DALL·E, and pronounced DOLL-E) are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions known as prompts. The first version of DALL-E was announced in January 2021. In the following year, its successor DALL-E 2 was released.
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Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.
Sora is a text-to-video model developed by OpenAI. The model generates short video clips based on user prompts, and can also extend existing short videos. Sora was released publicly for ChatGPT Plus and ChatGPT Pro users in December 2024. [1] [2]
Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [131] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [132] and reweighting training data. [133]