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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
CURE trains LLMs to reason about uncertainty at the claim level via a structured protocol and multi-stage calibration, improving factual accuracy by up to 39.9% on biography generation while boosting calibration metrics.
citing papers explorer
-
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.
-
Think Through Uncertainty: Improving Long-Form Generation Factuality via Reasoning Calibration
CURE trains LLMs to reason about uncertainty at the claim level via a structured protocol and multi-stage calibration, improving factual accuracy by up to 39.9% on biography generation while boosting calibration metrics.