distributional-modeling

This paper proposes Retrieval-Augmented Linguistic Calibration (RALC), a post-hoc pipeline for calibrating confidence signals in LLMs by modeling linguistic confidence as a distribution and using retrieval-augmented rewriting. It introduces Faithfulness Divergence metric and shows significant improvements across benchmarks.