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Temperature and top-p influence token sampling. They do not function as factuality switches.
Temperature changes how concentrated the next-token distribution is. Top-p limits sampling to a probability mass chosen from candidate tokens. Their effect depends on the model, other generation settings and the task. Lower temperature does not guarantee identical or correct answers.
A proposed experiment
Choose two tasks: producing a short factual answer from a provided passage and generating alternative headlines for a fictional event. Keep the model, input and output budget fixed.
For each setting you test, collect multiple outputs rather than judging a single response. Record adherence to the instructions, unsupported claims, useful variety and repetition. Change one setting at a time so you can interpret the result.
Questions that matter in production
- Does your provider expose the setting for this model?
- Is sampling enabled, or is another decoding strategy being used?
- Can your tests tolerate variation between runs?
- Does the extra variety actually improve the user outcome?
Avoid reporting a favourite setting as a universal recommendation. A creative-writing preference may be unsuitable for extracting a delivery date, and a model update may alter the trade-off.
Discussion: Which generation setting have you tested systematically, and what task-specific result surprised you?
Reference: Hugging Face generation configuration.
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