An example is Beam Search:https://www.width.ai/post/what-is-beam-search
Essentially we keep a window of probabilities of predicted tokens to improve the final quality of output.
Here is a blog post that describes it: https://huggingface.co/blog/how-to-generate.
I will warn you though that beam search is typically what you do NOT want. Beam search approximately optimizes for the "highest likely sequence at the token level." This is rarely what you need in practice with open-ended generations (e.g. a question-answering chat bot). In practice, you need "highest likely semantic sequence," which is much harder problem.
Of course, various approximations for semantic alignment are currently in the literature, but still a wide open problem.
(No affiliation)
I have no idea why you say this. Most of our pipelines will run greedy, for reproducibility.
Maybe we turn the temp up if we are returning conversational text back to a user.