Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty

• URL: http://arxiv.org/abs/2509.23002v1
• Date: Fri, 26 Sep 2025 23:40:47 GMT
• Title: Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty
• Authors: Lingyou Pang, Lei Huang, Jianyu Lin, Tianyu Wang, Akira Horiguchi, Alexander Aue, Carey E. Priebe,
• Abstract summary: We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
• Score: 49.19257648205146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying black-box LLMs requires managing uncertainty in the absence of token-level probability or true labels. We propose introducing an unsupervised conformal inference framework for generation, which integrates: generative models, incorporating: (i) an LLM-compatible atypical score derived from response-embedding Gram matrix, (ii) UCP combined with a bootstrapping variant (BB-UCP) that aggregates residuals to refine quantile precision while maintaining distribution-free, finite-sample coverage, and (iii) conformal alignment, which calibrates a single strictness parameter $\tau$ so a user predicate (e.g., factuality lift) holds on unseen batches with probability $\ge 1-\alpha$. Across different benchmark datasets, our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP, while consistently reducing the severity of hallucination, outperforming lightweight per-response detectors with similar computational demands. The result is a label-free, API-compatible gate for test-time filtering that turns geometric signals into calibrated, goal-aligned decisions.

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