How does temperature and sampling strategy affect ChatGPT output determinism and diversity?

Temperature and sampling strategies control randomness in token selection, balancing determinism and creativity.

ChatGPT generates outputs by sampling from probability distributions over tokens. Temperature scales logits before softmax: low temperature makes outputs more deterministic, while high temperature increases randomness.

Other strategies like top-k and nucleus (top-p) sampling further constrain token selection to the most likely candidates. These methods control diversity while maintaining coherence.

Production systems tune these parameters depending on use case: factual tasks use low temperature, while creative tasks use higher values.