Temperature vs. top_p in LLMs—how do they differ and what are sane defaults?
Asked on Sep 27, 2025
Answer
Temperature and top_p are two parameters used in language models to control the randomness and creativity of the generated text. They differ in how they influence the selection of the next word in a sequence.
Example Concept: Temperature is a parameter that scales the logits (raw predictions) before applying the softmax function, affecting the randomness of the output. A higher temperature results in more randomness, while a lower temperature makes the output more deterministic. Top_p, or nucleus sampling, selects from the smallest possible set of words whose cumulative probability exceeds the threshold p, ensuring that only the most likely words are considered, which can lead to more coherent outputs.
Additional Comment:
- Temperature values typically range from 0 to 1, where 0.7 is a common default for balanced creativity and coherence.
- Top_p values also range from 0 to 1, with 0.9 often used as a default to allow for diversity while maintaining coherence.
- Using both parameters together can fine-tune the balance between randomness and determinism in text generation.
- Experimentation is key to finding the right settings for specific applications or desired output styles.
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