Selective Risk Certification for LLM Outputs via Information-Lift Statistics: PAC-Bayes, Robustness, and Skeleton Design

• URL: http://arxiv.org/abs/2509.12527v2
• Date: Thu, 25 Sep 2025 01:55:13 GMT
• Title: Selective Risk Certification for LLM Outputs via Information-Lift Statistics: PAC-Bayes, Robustness, and Skeleton Design
• Abstract summary: We develop information-lift certificates that compare model probabilities to a skeleton baseline, accumulating evidence into PAC-Bayes valid under heavy-tailed bounds.<n>Across eight datasets, our method achieves 77.2% coverage at 2% risk, outperforming recent 2023-2024 baselines by 8.6-15.1 percentage points.
• Score: 6.908972852063454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models frequently generate confident but incorrect outputs, requiring formal uncertainty quantification with abstention guarantees. We develop information-lift certificates that compare model probabilities to a skeleton baseline, accumulating evidence into sub-gamma PAC-Bayes bounds valid under heavy-tailed distributions. Across eight datasets, our method achieves 77.2\% coverage at 2\% risk, outperforming recent 2023-2024 baselines by 8.6-15.1 percentage points, while blocking 96\% of critical errors in high-stakes scenarios vs 18-31\% for entropy methods. Limitations include skeleton dependence and frequency-only (not severity-aware) risk control, though performance degrades gracefully under corruption.

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