Model uncertainty isn't just about output confidence—it's also about whether confident predictions break down when you perturb the attention mechanisms that produced them. This fragility signal catches errors that confidence alone misses.
This paper introduces ASMI, a method to detect when large language models are uncertain about their predictions by measuring how fragile their answers are when attention pathways are perturbed. Unlike confidence scores alone, ASMI identifies 'confident-but-fragile' predictions that are likely wrong, cutting error rates in half on question-answering tasks without requiring multiple model samples.