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arXiv · 2509.23007

Taming Variability: Randomized and Bootstrapped Conformal Risk Control for LLMs

Lingyou Pang·Lei Huang·Jianyu Lin·Tianyu Wang·Alexander Aue·Carey E. Priebe
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Abstract

We transform the randomness of LLMs into precise assurances using an actuator at the API interface that applies a user-defined risk constraint in finite samples via Conformal Risk Control (CRC). This label-free and model-agnostic actuator manages ship/abstain/escalate actions based solely on a scalar score from opaque outputs. We enhance CRC's computational efficiency and robustness through Batched Bootstrap CRC (BB-CRC) and Randomized Batched Weighted-Average CRC (RBWA-CRC), reducing calibration calls and stabilizing thresholds while maintaining statistical validity. Additionally, we present a semantic quantification method grounded in gram matrix geometry, resulting in interpretable signal and metric design. Together these pieces deliver principled randomness control for LLM hallucination mitigation and LLM-as-judge reliability. Our framework is assessed using four datasets, demonstrating its efficacy in enhancing factual accuracy and measuring LLM-as-judge performance, yielding a simplified and computationally efficient control layer that converts variability into statistical validity.

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Lingyou Pang, Lei Huang, Jianyu Lin, Tianyu Wang, Alexander Aue, Carey E. Priebe. 2025-09-27. Taming Variability: Randomized and Bootstrapped Conformal Risk Control for LLMs. https://arxiv.org/abs/2509.23007

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