arXiv Machine Learning
From Behavior to Provenance: Attributing Tabular Foundation Models to Synthetic Pretraining Data
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Generation Provenance Before Behavior Attribution: Auditing Synthetic Speech Research Objects
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Source Identification Is Not Fitness Testing: Measuring the Limits of Synthetic-Data Attribution
The study investigates whether provenance information can reliably identify the source of synthetic text and whether this identification improves the selection of training data. Using financial‑risk text, the authors achieve 98.7% accuracy in attributing original generated passages, but accuracy drops to 53.1% after paraphrasing and 29.0% after style rewriting. They compare two selection strategies—one based on source provenance and another on a reference model score—across three rounds of generation and retraining, finding that the two methods choose different examples but do not produce a consistent difference in model degradation. The results suggest that source attribution and useful data selection are distinct challenges, and neither provenance nor the tested proxy suffices to guarantee stable recursive training behavior.