Task exchangeability framework enables statistically valid inference from synthetic data
A new arXiv preprint proposes a statistical framework for using synthetic data in scientific research with provable validity guarantees, centered on a condition called 'task exchangeability.' The framework requires identifying historical tasks with real data that are exchangeable with the current task of interest, enabling valid inference even when synthetic data is biased or misspecified. The authors demonstrate the approach on LLM-generated 'silicon samples' for public opinion surveys and LLM-as-a-judge AI evaluation settings. This addresses a foundational concern about the reliability of synthetic data pipelines increasingly used across AI evaluation and scientific research.
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