arXiv AI
Jun 17

Combating Data Laundering in LLM Training

arXiv:2604. 01904v3 Announce Type: replace-cross Abstract: Post-hoc unauthorized-training data detection for large language models (LLMs) typically assumes a query-with-originals regime: rights holders query a target LLM with raw proprietary data and assess whether the model assigns them stronger memorization-based detection signals, e.

By Muxing Li, Zesheng Ye, Sharon Li, Feng Liu
arXiv Machine Learning
1d ago

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.

By Joss Armstrong