LLM temperature: not creativity, but probability distribution control

The LLM temperature parameter is often misunderstood as a creativity dial, but it actually controls how the model's probability distribution is reshaped before sampling the next token. A temperature of 1.0 uses the distribution as-is, while lower temperatures sharpen the distribution by favoring more probable tokens, and higher temperatures flatten it by giving less probable tokens a greater chance of being selected. Developers should default to a temperature of 0 for tasks requiring correctness, such as classification or structured output, and only increase it when variance is desired, like in open-ended generation. AI

IMPACT Clarifies the function of the temperature parameter in LLMs, guiding developers to use it more effectively for specific tasks.

RANK_REASON The item explains a technical concept related to LLMs without announcing a new model or product.

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• Softmax
• temperature

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