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WildTrace: Evaluating Training Data Detection in Realistic Large-Scale LLM Post-Training

L5 · ResearcherResearcharXiv· 10/12/2026

Critical for ML researchers working on data provenance, model transparency, and copyright compliance in LLM training pipelines.

AI Summary

Researchers introduce WildTrace, a new showing current data detection methods perform near random (0.529 AUC) in realistic large-scale LLM post-training scenarios.

Excerpt

Although post-training significantly enhances model performance, the use of massive undisclosed data raises concerns about benchmark contamination and copyright infringement. This drives growing interest in training data detection for post-trained models. Existing evaluations for training data detection often leverage benchmarks constructed post hoc, which can introduce distribution shifts. Alternatively, controlled evaluations rely on small-scale training setups that diverge from realistic post

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