RALC is a retrieval-augmented rewriting pipeline that improves linguistic faithfulness and calibration of LLM outputs by up to 66% and 58% on QA benchmarks.
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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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
RAC adds ranking-aware group loss and clean-corrupted pairwise loss to RL post-training to boost both accuracy and calibration in multimodal reasoning without extra annotations.
citing papers explorer
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Retrieval-Augmented Linguistic Calibration
RALC is a retrieval-augmented rewriting pipeline that improves linguistic faithfulness and calibration of LLM outputs by up to 66% and 58% on QA benchmarks.
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Ranking-Aware Calibration for Reliable Multimodal Reinforcement Learning
RAC adds ranking-aware group loss and clean-corrupted pairwise loss to RL post-training to boost both accuracy and calibration in multimodal reasoning without extra annotations.