Statistical Methods in Generative AI (huggingface.co)

This review surveys how classical and modern statistical methods can be applied to make generative AI systems more reliable, higher quality, and more efficient to evaluate. It starts from the central observation that generative models are fundamentally samplers from probabilistic models and, as such, provide no built‑in guarantees about correctness, safety, or fairness. The paper synthesizes existing work that leverages statistics to add rigorous checks and controls—improving calibration, quantifying uncertainty, detecting distributional shift, and supporting principled evaluation and experimental design for interventions and A/B tests. Technically, the authors explain a toolbox of approaches—calibration and uncertainty quantification (including conformal-style guarantees), hypothesis testing and selective inference for safety/fairness audits, importance sampling and variance-reduction for efficient evaluation, and statistical experimental design for causal and policy interventions—and map each to concrete generative-AI use cases. They also spell out limitations: model misspecification, high-dimensional sampling costs, reliance on unverifiable assumptions, and the gap between probabilistic guarantees and downstream human values. The paper calls for scalable, theoretically grounded methods that integrate causal reasoning and societal metrics, plus benchmarks and tooling to translate statistical guarantees into practical safeguards for large generative systems.