Statistical Methods in Generative AI
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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Edgar Dobriban
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2025-09-12T16:11:44.387Z
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Edgar Dobriban
user
ed13
Statistical Methods in Generative AI
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.
Statistical methods are reviewed for improving the reliability, quality, and efficiency of generative AI techniques, highlighting their applications and limitations.