用统计方法提升生成式AI的可靠性与评估效率
Statistical Methods in Generative AI
- 引入统计技术增强生成模型的可信度与安全性
- 改进AI评估质量与实验设计效率
- 适合关注AI可信赖性的研究者与开发者
生成式人工智能正成为一项重要技术，有望在多个领域产生变革性影响。然而，生成式AI基于概率模型采样，天然缺乏对正确性、安全性、公平性等属性的保证。统计方法为提升生成式AI的可靠性提供了潜在途径。此外，统计方法还可在提高AI评估质量与效率、设计干预措施与实验方面发挥重要作用。本文综述了该领域的现有工作，阐释了通用统计技术及其在生成式AI中的应用，并讨论了当前局限与未来方向。
原文摘要 · Abstract (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.
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