SAGE: New Method to Improve How Language Models Express Uncertainty
Researchers have introduced SAGE (Semantic-Answer Guided Entropy), a new training framework designed to make large language models' verbal expressions of uncertainty more accurately reflect their underlying behavior. The work addresses a known gap where LLMs often state confidence levels that do not match what repeated sampling of the model would actually show. Better-calibrated uncertainty expressions could improve the reliability and trustworthiness of AI systems in high-stakes applications.
A preprint posted to arXiv presents SAGE, a group-level uncertainty target that aims to align how large language models verbally express uncertainty with their actual distributional behavior across repeated outputs. The authors frame verbal uncertainty alignment as a calibration problem, arguing that appropriate uncertainty targets should be derived from multiple model samples rather than a single response. SAGE constructs an answer-conditioned uncertainty geometry that preserves distinctions between categorical, numeric, and symbolic answer types while maintaining a smooth, scale-preserving calibration signal. The framework is applied through a training method called Group-Uncertainty Preference Optimization (GUPO), which supervises only the uncertainty-expressing channel of a response rather than the full output. Experiments spanning factual, mathematical, and multiple-choice reasoning tasks reportedly show improvements in uncertainty ranking, lower calibration error, and reduced overconfidence compared to existing approaches.
What's missing
It is unclear how SAGE performs in open-ended or long-form generation tasks beyond the structured benchmarks evaluated. The work is a preprint and has not yet undergone peer review.
What different sources said
- arXiv cs.CLCenter
SAGE: Answer-Conditioned Uncertainty Targets for Verbal Uncertainty Alignment
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