arXiv:2509.20345v3 Announce Type: replace-cross Abstract: The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and challenges for statistical inference. This paper introduces a GEneral Synthetic-Powered Inference (GESPI) framework that wraps around a broad class of statistical inference procedures to safely enhance sample efficiency by combining synthetic and real data. Our framework leverages high-quality synthetic data to boost statistical power, yet adaptively defaults to th
The proliferation of high-quality synthetic data from advanced AI models is accelerating, necessitating new frameworks for its integration into statistical inference to enhance efficiency.
This framework offers a method to significantly boost statistical power and sample efficiency by combining synthetic and real data, which is critical for areas with data scarcity or high collection costs.
Statistical inference procedures can now be safely and adaptively enhanced with synthetic data, moving beyond traditional real-data constraints and accelerating research and development cycles.
- · AI model developers
- · Data-scarce industries (e.g., healthcare, specialized manufacturing)
- · Research institutions
- · Companies with proprietary data that can be augmented with synthetic data
- · Traditional statistical methods reliant solely on real data
- · Data collection companies if synthetic data reduces their market
Increased efficiency and lower costs in data analysis and model training across various domains.
Faster innovation cycles in science and industry due to enhanced data utilization and reduced experimental overhead.
Ethical and regulatory debates intensify regarding the provenance and validity of synthetic data in high-stakes decision-making contexts.
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Read at arXiv cs.LG