The answer
No. Temperature reshapes the probability distribution used in sampling. Candidate tokens remain influenced by the model’s scores and by any additional filtering rules.
Why doesn’t an LLM always select the token with the highest probability?
Is temperature the same as choosing a random word?
More FAQs in Why doesn’t an LLM always select the token with the highest probability?
No. It selects the highest-scoring continuation under the model, which can still produce a false or unsupported statement if that continuation is favored.
Yes. A token’s local probability does not fully measure the usefulness of the complete continuation. Different early choices can lead to substantially different later sequences.
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