Deep learning has been deployed in many tasks in NLP, such as translation, image captioning, and dialogue systems. In machine translation, it is used to read source language (input) and generate the desired language (output). Similarly in a dialogue system, it is used to generate a response given a context. This is also known as Natural Language Generation (NLG).
This article explains what temperature is in the context of language models, how it works, its relationship to beam search, and how output generation can still go haywire despite these techniques.
