LLMs struggle to associate epistemic markers with stable internal confidence levels across distributions, even under model-centric interpretations, while maintaining somewhat consistent marker rankings.
Quantifying uncertainty in answers from any language model and enhancing their trustworthiness
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RLMF uses quality of model self-judgments to refine RL rankings and select training data, achieving SOTA faithful calibration while preserving accuracy and outperforming standard RL by up to 63%.
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.
Clustered Self-Assessment groups sampled LLM responses into semantic clusters, presents clusters as multiple-choice options, and uses the LLM's assigned probabilities to those options as direct uncertainty estimates, outperforming entropy baselines with as few as two extra samples.
DPUA is a two-phase framework that aligns LLM uncertainty expressions with human disagreement distributions in subjectivity analysis while preserving task performance.
Separate modality-specific reasoning before fusion reduces hallucinations and improves accuracy in audio-visual LLMs by enforcing isolated traces then integrating evidence.
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