score-reasoning-failure-cross-layer-attention-entropy-dispersion TEC-0198 · proposedScore reasoning failure by cross-layer attention-entropy dis
At each generation step, average attention entropy per layer, take the standard deviation across layers, and aggregate these over the sequence as features for a logistic-regression hallucination classifier.
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
At each generation step, average attention entropy per layer, take the standard deviation across layers, and aggregate these over the sequence as features for a logistic-regression hallucination classifier. Filed by the drafting stage from Attention Dispersion as a Diagnostic Signal for Hallucination in Large Language Models (2609.18320) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: ROC-AUC under repeated stratified 5-fold cross-validation (10 repeats), against output-logit baseline features, with 95% CIs and one-sided Wilcoxon signed-rank tests; gains of +0.0093 to +0.0759 AUC.. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
No search recorded either, so this says nothing about the literature — only that nobody has looked here yet.
Code
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Sources
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