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OriginBlame is a record- and token-level data provenance system that propagates author identity through AI training data pipelines, enabling precise forget sets for machine unlearning. It eliminates over-deletion from dataset-level systems and improves unlearning effectiveness.
This paper introduces a novel dataset watermarking method for closed LLMs that uses co-occurrence patterns of word pairs to provably detect if proprietary data was used in training, even when it constitutes a small fraction of the dataset.