Tag
This paper introduces DRIVE, a unified Transformer-based framework for offline auto-bidding that decouples candidate action generation from decision making, combining distributional action modeling, retrieval-augmented candidate generation, and value-based evaluation to improve bidding performance under budget and cost constraints.
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.