Task calibration aligns LLM distributions in latent task spaces to make MBR decoding provably optimal and improve generation quality.
Conftuner: Training large language models to express their confidence verbally
6 Pith papers cite this work. Polarity classification is still indexing.
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A composite loss with Brier calibration, anchor regularization, contrastive alignment from 2x2 perturbations, and KL stabilization reduces calibration error by over 60% in medical VQA while preserving accuracy.
A new framework quantifies faithful confidence expression in large reasoning models by comparing linguistic decisiveness to token probabilities, hidden states, and response consistency, revealing it as a persistent challenge.
CoMet decomposes MLLM uncertainty into context-specific and multiplicity-specific terms estimated by a trained post-hoc module, improving performance on open-ended multimodal benchmarks and hallucination detection.
The paper introduces an optimization framework for AI agents to strategically seek support, proving a threshold policy on support value and providing an online algorithm to control missed-support error without distributional assumptions.
Fine-tuning Gemma 3 4B on unfiltered self-consistency targets produces a binary verbal correctness discriminator with AUROC 0.774 on TriviaQA, outperforming logit entropy after a modal-filtered pre-registration failed.
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Task-Aware Calibration: Provably Optimal Decoding in LLMs
Task calibration aligns LLM distributions in latent task spaces to make MBR decoding provably optimal and improve generation quality.