The Effect of Knowledge Editing Methods on Confidence Calibration

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Key Points

To explore how knowledge editing methods affect confidence calibration in language models.
Applied three types of knowledge editing methods to ten language models.
Measured the degree of divergence between confidence and accuracy pre- and post-knowledge editing.
Analyzed the impact of knowledge editing on token prediction probabilities and correctness.
In tasks requiring understanding of sentence meaning, successful edits resulted in a smaller increase in token prediction probabilities compared to actual accuracy gains.
Knowledge editing methods tended to lead to lower confidence levels than accuracy gains, contrary to trends observed in reinforcement learning.
Successful knowledge editing after RLHF indicated potential for improved confidence calibration.

Investigates the effect of knowledge editing on confidence accuracy in language models, suggesting implications for model performance.

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Hasegawa et al. (2026) studied this question.

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