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SAGE introduces a novel training framework that aligns verbal uncertainty expressions with actual model sampling distributions by constructing answer-conditioned entropy targets. This approach addresses the critical gap where current models fail to reflect their true confidence levels, offering a more robust calibration signal for factual and reasoning tasks. By supervising uncertainty channels separately from full responses, the method effectively reduces overconfidence and improves reliability across diverse problem types.
SAGE: Answer-Conditioned Uncertainty Targets for Verbal Uncertainty Alignment
arXiv:2606.11512v1 Announce Type: new Abstract: Large language models increasingly express uncertainty through natural-language statements, yet these expressions often fail to reflect the model's sampled behavior. We study verbal uncertainty alignment as a distributional calibration problem: the appropriate uncertainty target for a prompt should be estimated from repeated model outputs rather than from an isolated response. However, group rollouts alone are insufficient, since the resulting target must provide a useful training signal. Existing targets only partially satisfy this requirement. We propose SAGE, Semantic-Answer Guided Entropy, a group-level uncertainty target that constructs an answer-conditioned uncertainty geometry over sampled responses. SAGE preserves categorical, numeric, and symbolic answer distinctions while maintaining a smooth and scale-preserving calibration signal. We further apply this target through Group-Uncertainty Preference Optimization, or GUPO, an uncertainty-channel training framework that supervises verbal uncertainty expressions rather than the full response. Experiments across factual, mathematical, and multiple-choice reasoning tasks show improved uncertainty ranking, lower calibration error, and reduced overconfidence.
Abstract:Large language models increasingly express uncertainty through natural-language statements, yet these expressions often fail to reflect the model's sampled behavior. We study verbal uncertainty alignment as a distributional calibration problem: the appropriate uncertainty target for a prompt should be estimated from repeated model outputs rather than from an isolated response. However, group rollouts alone are insufficient, since the resulting target must provide a useful training signal. Existing targets only partially satisfy this requirement. We propose SAGE, Semantic-Answer Guided Entropy, a group-level uncertainty target that constructs an answer-conditioned uncertainty geometry over sampled responses. SAGE preserves categorical, numeric, and symbolic answer distinctions while maintaining a smooth and scale-preserving calibration signal. We further apply this target through Group-Uncertainty Preference Optimization, or GUPO, an uncertainty-channel training framework that supervises verbal uncertainty expressions rather than the full response. Experiments across factual, mathematical, and multiple-choice reasoning tasks show improved uncertainty ranking, lower calibration error, and reduced overconfidence.
Computation and Language (cs.CL)
arXiv:2606.11512 [cs.CL]
(or arXiv:2606.11512v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.11512 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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