Which family of models developed by OpenAI generates human-like text from short prompts?

The GPT models, or Generative Pre-trained Transformers, are specifically designed to generate human-like text based on short prompts. They function by predicting the next word in a sequence, drawing on a vast amount of pre-existing text data to create coherent and contextually relevant sentences. This capability stems from the architecture and training approach used in these models, which allows them to understand language patterns and nuances effectively.

In contrast, RNN models, or Recurrent Neural Networks, are primarily used for sequential data but may struggle with long-range dependencies and require more complex architectures for tasks like text generation. Transformer models, while foundational to the architecture of GPT, refer more broadly to the framework itself that encompasses various implementations beyond just the GPT series. CNN models, or Convolutional Neural Networks, are predominantly utilized in image processing and are not typically employed for text generation tasks.

Hence, the GPT models exemplify a specific development aimed at generating text in a manner that closely resembles human writing, distinguishing them as the correct choice in this context.