Calibration vs Decision Making: Revisiting the Reliability Paradox in Unlearned Language Models

TL;DR

This paper investigates the relationship between calibration and decision-making reliability in language models, revealing that good calibration can coexist with reliance on spurious correlations even after unlearning.

Contribution

It demonstrates that calibration error alone does not guarantee reliable decision rules in unlearned language models, highlighting the reliability paradox in this context.

Findings

Fine-tuned models have low calibration error (~0.04) compared to pretrained models (>0.5).

Models after unlearning retain low calibration error despite reduced accuracy.

Attribution analysis shows increased reliance on correlation-based tokens post-unlearning.

Abstract

Machine unlearning aims to remove the influence of specific training data from a model while preserving reliable behavior on the remaining data, making reliable prediction and uncertainty estimation essential for evaluation. Calibration is commonly used as a proxy for reliability in language models, but low calibration error does not necessarily imply reliable decision rules, as models may rely on spurious correlations while remaining well calibrated. We investigate this gap in generative language models using the multiple-choice question-answering evaluation protocol on the TOFU benchmark, measuring probabilistic reliability with calibration metrics (ECE, MCE, Brier) and decision-rule reliability via attribution-based shortcut detection with Integrated Gradients and Local Mutual Information. We find that fine-tuned models achieve low calibration error (ECE ~ 0.04) compared to pretrained models (ECE > 0.5), and models after unlearning retain similarly low calibration despite reduced accuracy on the forget split, while attribution analysis shows increased reliance on correlation-based tokens. These results demonstrate that good calibration can coexist with shortcut-based decision rules after unlearning, extending the reliability paradox to the machine unlearning setting.