How does the 'temperature' parameter influence the diversity and predictability of GPT model outputs, and when would you favor a lower temperature?

The 'temperature' parameter in GPT models controls the randomness and thus the diversity of the generated text. It essentially scales the probability distribution of the next possible tokens. A higher temperature (typically between 0 and 1, but can sometimes exceed 1) makes the model more likely to sample from less probable tokens, leading to more diverse, creative, and sometimes surprising outputs. Conversely, a lower temperature makes the model more likely to s....