AURA is an adaptive uncertainty-aware refinement method for auditing LLM-as-a-judge pairwise decisions that learns human-consistency signals through selective human verification on uncertain cases.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A cognitive-uncertainty guided two-stage KD framework filters to 10.3% of samples to reach 0.9585 MAP@3 and 84.38% accuracy with a 4B model, beating larger LLMs on misconception classification.
Informativeness and diversity of samples selected by active learning show no correlation with test performance on translation tasks using few samples; ordering and pre-training effects dominate instead.
citing papers explorer
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AURA: Adaptive Uncertainty-aware Refinement for LLM-as-a-Judge Auditing
AURA is an adaptive uncertainty-aware refinement method for auditing LLM-as-a-judge pairwise decisions that learns human-consistency signals through selective human verification on uncertain cases.
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Cognitive-Uncertainty Guided Knowledge Distillation for Accurate Classification of Student Misconceptions
A cognitive-uncertainty guided two-stage KD framework filters to 10.3% of samples to reach 0.9585 MAP@3 and 84.38% accuracy with a 4B model, beating larger LLMs on misconception classification.
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Testing the Assumptions of Active Learning for Translation Tasks with Few Samples
Informativeness and diversity of samples selected by active learning show no correlation with test performance on translation tasks using few samples; ordering and pre-training effects dominate instead.