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 select the most probable tokens, resulting in more predictable, focused, and conservative outputs. A lower temperature is favored when accuracy, precision, and coherence are paramount. For tasks requiring factual correctness or adherence to specific guidelines, a lower temperature minimizes the risk of the model generating incorrect or nonsensical information. For example, when generating code, translating text, or summarizing a document, a lower temperature ensures that the output is accurate and consistent with the input. Lower temperatures are also preferred when you want the model to follow a specific pattern or style consistently. If you are generating product descriptions using a predefined template, a lower temperature will ensure that the generated descriptions adhere to the template and maintain a consistent tone and style. Essentially, lowering the temperature reduces the model's 'creativity' in favor of reliability and accuracy. A temperature of 0 will often result in the most predictable and deterministic output, consistently choosing the highest probability token.