Estimating Text Temperature

ACL ARR 2026 January Submission5508 Authors

Keywords: Sampling temperature, LLM, maximum likelihood

Abstract: Autoregressive language models typically use temperature parameter at inference to shape the probability distribution and control the randomness of the text generated. After the text was generated, this parameter can be estimated using maximum likelihood approach. Following it, we propose a procedure to estimate the temperature of any text, including ones written by humans, with respect to a given language model. We evaluate the temperature estimation capability of a wide selection of small-to-medium LLMs. We then use the best-performing Qwen3 14B to estimate temperatures of popular corpora.

Paper Type: Short

Research Area: Natural Language Generation

Research Area Keywords: analysis

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models, Data analysis, Theory

Languages Studied: English

Submission Number: 5508