A language model gives an inaccurate answer, and someone suggests turning the temperature down. That setting can change the way text is sampled, but it does not consult an external fact-checker or repair missing knowledge.
Hugging Face’s generation documentation describes temperature as a parameter that modifies next-token probabilities. Its effect depends on the generation method and the other settings in use. Some systems expose different controls or do not use temperature in a given mode.
Separate variation from correctness
With sampling enabled, a lower temperature generally concentrates selection toward higher-probability options, while a higher value can produce more variation. A probable continuation can still contain a false date, an invented source or a misunderstanding of the request.
Consider a hypothetical product-description task. You may prefer restrained variation so the model follows a house style. That choice does not establish that the material, dimensions or warranty it mentions are correct. Those facts must come from reliable product data and be checked against it.
Test one change at a time
Keep the prompt, model version, source material and other generation settings fixed while comparing outputs. Use several examples rather than choosing the nicest result from a single run. Record whether the change affects factual errors, omitted requirements or merely wording.
Do not promise exact repeatability just because a temperature is low. The service, model implementation and sampling configuration may introduce other differences. Read the documentation for the system you actually use.
For factual work, give the model appropriate evidence, require it to distinguish uncertainty and verify important claims independently. For creative work, judge whether variation serves the brief. Temperature is a control over generation behaviour; the quality standard still has to be defined and tested by the people using the output.