OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets
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.
- ▪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
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Computer Science > Artificial Intelligence arXiv:2607.13037 (cs) [Submitted on 19 May 2026] Title:OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets Authors:Haolin Xue View a PDF of the paper titled OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets, by Haolin Xue View PDF HTML (experimental) 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.
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