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

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TL;DR - An arXiv preprint proposing to fix LLM overconfidence from preference alignment during training rather than with post-hoc temperature scaling, by using bilevel optimization to maximize predictive entropy. It matters because calibration fitted post-hoc on one domain doesn't transfer, while this approach targets out-of-domain generalization.

  • Frames calibration as maximizing the entropy of predictive distributions, directly penalizing overly concentrated (overconfident) predictions.
  • Uses a bilevel formulation inspired by temperature scaling: the lower level trains the model under a parametric loss, the upper level selects loss hyperparameters to maximize entropy.
  • Applies an efficient first-order approximation to avoid explicit second-order computation, making it tractable at LLM scale.
  • Evaluated on multiple-choice and open-ended generative QA, reporting better-calibrated models with particular gains out-of-domain (no numeric results given in the abstract).

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

arXiv cs.LG Ruochen Jin, Zhanliang Wang, Zongyu Dai, Jiancong Xiao, Bojian Hou 2026-08-07 arXiv:2608.07419
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-09 08:18:28.117410 UTC

TL;DR - An arXiv preprint proposing to fix LLM overconfidence from preference alignment during training rather than with post-hoc temperature scaling, by using bilevel optimization to maximize predictive entropy. It matters because calibration fitted post-hoc on one domain doesn't transfer, while this approach targets out-of-domain generalization.

  • Frames calibration as maximizing the entropy of predictive distributions, directly penalizing overly concentrated (overconfident) predictions.
  • Uses a bilevel formulation inspired by temperature scaling: the lower level trains the model under a parametric loss, the upper level selects loss hyperparameters to maximize entropy.
  • Applies an efficient first-order approximation to avoid explicit second-order computation, making it tractable at LLM scale.
  • Evaluated on multiple-choice and open-ended generative QA, reporting better-calibrated models with particular gains out-of-domain (no numeric results given in the abstract).
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