This paper provides a comprehensive overview of Large Language Models specifically tailored for statisticians, highlighting intersections between statistical principles and modern AI systems.

• Security and trust challenges including privacy preservation, watermarking techniques, fairness metrics, and trustworthiness evaluation
• Statistical foundations needed to address emerging LLM problems in uncertainty quantification and causal inference
• Decision-making capabilities and methods to handle distribution shifts in LLM applications
• Educational value for statisticians entering the AI field through a familiar statistical lens

For security professionals, this research clarifies how statistical approaches can strengthen LLM robustness against threats while providing frameworks for responsible AI deployment in sensitive contexts.

An Overview of Large Language Models for Statisticians