The process that is used to generate answers in language models involves machine learning and natural language processing with the following steps:
1. Tokenization: The first step is to divide text of prompt into individual words or tokens.
2. Encoding: Each token is then converted into its numerical encoding.
3. Contextualization: The relationship between tokens and the text is determined by the neural network.
4. Prediction: The language model generates prediction for the answer to the prompt using probability analysis.
5. Selection: Best answer is selected from probable answers as response.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence