Temperature Fragility and the Conditional Benefits of Truncation Sampling

Abstract:Large language models generate text by sampling each token from a predicted distribution, and a temperature parameter sets how far the draw strays from the most probable tokens. Truncation samplers such as top-p and min-p discard the least probable tokens before the draw, so that sampling at high temperature stays coherent. Their reported accuracy gains come from temperatures of 1.5 to 3, while the defaults of deployed systems cluster between 0.6 and 1.0. Whether they change accuracy at those defaults, and for which models, has not been measured. We test thirteen open-weight models on GSM8K and MMLU-Pro at temperatures 0.7, 1.0, and 1.3 in one controlled pipeline, ten of them under eight decoding configurations. Six of the thirteen models lose 17 to 38 accuracy points on MMLU-Pro between 0.7 and 1.3, and the other seven lose at most 10. The lost accuracy comes from generations that run to the token limit or never state an answer. These results suggest that truncation samplers improve accuracy primarily when higher temperatures substantially degrade model performance. Where accuracy remains stable across temperatures, none of the tested truncation samplers improves on plain temperature sampling.

20 pages, 8 figures, 16 tables. Code and data: this https URL

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.15476 [cs.CL]

(or arXiv:2609.15476v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.15476 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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