Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration

Preference alignment often makes large language models (LLMs) overconfident and poorly calibrated. Traditional post-hoc temperature scaling is inherently domain-dependent: a temperature fitted on one domain does not generalize across domains. This motivates us to modify model parameters during training to improve calibration. We propose maximizing the entropy of predictive distributions as the calibration objective, which directly targets overconfidence by discouraging overly concentrated predic

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ethics.ai (7 August 2026), “Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration,” evidence record 17937, https://ethics.ai/record/17937 (originally published by arXiv cs.LG).

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