生成式人工智能中的统计方法

摘要

生成式人工智能正逐渐成为一项重要技术，有望在众多领域带来变革。然而，生成式AI技术本质上基于概率模型的采样，默认情况下，它们无法保证正确性、安全性、公平性或其他属性。统计方法为提高生成式AI技术的可靠性提供了一条有前景的潜在途径。此外，统计方法在提升AI评估的质量与效率，以及设计AI干预措施和实验方面也展现出巨大潜力。本文回顾了这些主题下的现有研究，既阐述了所采用的通用统计技术，也探讨了它们在生成式AI中的应用。同时，我们还将讨论当前研究的局限性和未来可能的发展方向。

English

Generative Artificial Intelligence is emerging as an important technology, promising to be transformative in many areas. At the same time, generative AI techniques are based on sampling from probabilistic models, and by default, they come with no guarantees about correctness, safety, fairness, or other properties. Statistical methods offer a promising potential approach to improve the reliability of generative AI techniques. In addition, statistical methods are also promising for improving the quality and efficiency of AI evaluation, as well as for designing interventions and experiments in AI. In this paper, we review some of the existing work on these topics, explaining both the general statistical techniques used, as well as their applications to generative AI. We also discuss limitations and potential future directions.

生成式人工智能中的统计方法

Statistical Methods in Generative AI

摘要

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