The Necessity of Setting Temperature in LLM-as-a-Judge
Large Language Models (LLMs) are increasingly used as automated judges, yet decoding temperature is often set by intuition rather than evidence. We run over one million large scale evaluations, multiple open-source models, prompting strategies, and judging paradigms to examine how temperature affects judge reliability. Low temperatures yield consistent outputs with few errors, especially in smaller models, while higher temperatures reduce consistency and increase errors. Yet consistency alone does not guarantee sound judgments: low temperatures can narrow the range of reasoning explored. We characterize judge behavior through four reasoning personas and find that temperature shifts their ...
| Authors | Lujun Li, Lama Sleem, Yangjie Xu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 March 2026 |
| Last updated at source | 15 October 2026 |
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