OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets
OriginBlame is a record- and token-level data provenance system that precisely resolves data removal requests to individual training records, reducing over-deletion from 101x to 1.3x on Wikipedia data. Integration adds 1.3-4.0% throughput overhead (HuggingFace) and 2.1-19.0% (Datatrove). On a 1.7B model, provenance-based forget sets improve unlearning by 42% over random baselines.
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[Submitted on 19 May 2026]
Title:OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets
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Abstract:When a data contributor requests removal, model trainers face a practical gap: unlearning algorithms require a forget set, yet no tool can locate which training records belong to a given author. Existing provenance systems operate at file or dataset level, forcing catastrophic over-deletion. We present ob, a record- and token-level data provenance system that propagates author identity through data processing pipelines and resolves revocation requests into precise forget sets via deterministic queries. Evaluation on 219,555 Wikipedia pages demonstrates that record-level provenance eliminates dataset-level over-deletion (from 101x to 1.3x), while integration adds 1.3-4.0% throughput overhead (HuggingFace) and 2.1-19.0% (Datatrove) on wiki data. On a 1.7B model, provenance-based forget sets improve unlearning by 42% over random baselines.
Comments: 13 pages, 6 figures
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.13037 [cs.AI]
(or arXiv:2607.13037v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.13037
arXiv-issued DOI via DataCite
Submission history
From: Haolin Xue [view email] [v1] Tue, 19 May 2026 10:18:33 UTC (81 KB)
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