LaaB improves LLM hallucination detection by mapping self-judgment labels back into neural feature space and using mutual learning under logical consistency constraints between responses and meta-judgments.
Calibrating verbal uncertainty as a linear feature to reduce hallucinations
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Architecture and training determine whether transformers retain a readable internal signal that lets activation monitors catch errors missed by output confidence.
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Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments
LaaB improves LLM hallucination detection by mapping self-judgment labels back into neural feature space and using mutual learning under logical consistency constraints between responses and meta-judgments.
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Architecture Determines Observability of Transformers
Architecture and training determine whether transformers retain a readable internal signal that lets activation monitors catch errors missed by output confidence.