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Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing, machine translation, and natural language generation and can be used as foundation models for other tasks. [62]
GPT-4 is a multi-modal LLM that is capable of processing text and image input (though its output is limited to text). [49] Regarding multimodal output , some generative transformer-based models are used for text-to-image technologies such as diffusion [ 50 ] and parallel decoding. [ 51 ]
Generative Pre-trained Transformer 3.5 (GPT-3.5) is a sub class of GPT-3 Models created by OpenAI in 2022. On March 15, 2022, OpenAI made available new versions of GPT-3 and Codex in its API with edit and insert capabilities under the names "text-davinci-002" and "code-davinci-002". [ 28 ]
Once you locate text sources, such as webpages and PDF documents, upload them to your chat tool of choice and start asking the AI questions about the docs. ... In Claude 3, for example, this ...
Also, some special symbols are used to denote special text formatting. For example, "Ġ" denotes a preceding whitespace in RoBERTa and GPT. "##" denotes continuation of a preceding word in BERT. [24] For example, the BPE tokenizer used by GPT-3 (Legacy) would split tokenizer: texts -> series of numerical "tokens" as
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.
Early examples of foundation models are language models (LMs) like OpenAI's GPT series and Google's BERT. [3] [4] Beyond text, foundation models have been developed across a range of modalities—including DALL-E and Flamingo [5] for images, MusicGen [6] for music, and RT-2 [7] for robotic control.
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