Text-to-Text (T2T) Model

A Text-to-Text (T2T) Model is a unimodal text-to-* generative model that accepts a text and can produce text data.

• Context:
  • It can (typically) be referenced by a Text-to-Text System.
  • It can (typically) be produced by a Text-to-Text Model Training System.
  • It can be associated to a Text-to-Speech Model.
  • It can be evaluated by a Text-to-Text Model Benchmark Task.
  • ...
• Example(s):
  • GPT-4, GPT-3.
  • PaLM.
  • ...
  • …
• Counter-Example(s):
  • Text-to-Code Model.
  • Text-to-Image Model, or a Text-to-Sound Model.
  • an Text Denoising Model.
• See: High-Quality Image, Seq2Seq Model.

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References

2023

• chat
  • A Text-to-Text model is a type of generative model in machine learning that accepts one or more textual inputs and produces one or more textual outputs, typically using sequence-to-sequence learning. These models have various applications in natural language processing (NLP), such as machine translation, summarization, question-answering, and conversational AI.
    • It can take one or more textual inputs and generate one or more textual outputs.
    • It can be trained on large datasets using supervised learning techniques.
    • It can use deep learning architectures such as transformers, LSTMs, or RNNs.
    • It can generate coherent and high-quality natural language responses.

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