GPT basically reads the text you have input, and generates a set of 'likely' next words (technically 'tokens').
So for example, the input:
Bears like to eat ________
GPT may effectively respond with Honey (33% likelihood that honey is the word that follows the statement) and Humans (30% likelihood that humans is the word that follows this statement). GPT is just estimating what word follows next in the sequence based on all it's training data.
With temperature = 0, GPT will always choose "Honey" in the above example.
With temperature != 0, GPT will add some randomness and would occasionally say "Bears like to eat Humans" in the above example.
Strangely a bit of randomness seems to be like adding salt to dinner - just a little bit makes the output taste better for some reason.
So for example, the input:
Bears like to eat ________
GPT may effectively respond with Honey (33% likelihood that honey is the word that follows the statement) and Humans (30% likelihood that humans is the word that follows this statement). GPT is just estimating what word follows next in the sequence based on all it's training data.
With temperature = 0, GPT will always choose "Honey" in the above example.
With temperature != 0, GPT will add some randomness and would occasionally say "Bears like to eat Humans" in the above example.
Strangely a bit of randomness seems to be like adding salt to dinner - just a little bit makes the output taste better for some reason.