Large language models (LLMs) are deployed at scale, yet their training data life cycle remains opaque. This survey synthesizes research from the past ten years on three tightly coupled axes: (1) data provenance, (2) transparency, and (3) traceability, and three supporting pillars: (4) bias \& uncertainty, (5) data privacy, and (6) tools and techniques that operationalize them. A central contribution is a proposed taxonomy defining the field's domains and listing corresponding artifacts. Through analysis of 95 publications, this work identifies key methodologies concerning data generation, watermarking, bias measurement, data curation, data privacy, and the inherent trade-off between transparency and opacity.
@article{arxiv.2601.14311,
title = {Tracing the Data Trail: A Survey of Data Provenance, Transparency and Traceability in LLMs},
author = {Richard Hohensinner and Belgin Mutlu and Inti Gabriel Mendoza Estrada and Matej Vukovic and Simone Kopeinik and Roman Kern},
journal= {arXiv preprint arXiv:2601.14311},
year = {2026}
}
Comments
35 pages, 6 figures. Manuscript submitted to ACM Computing Surveys (CSUR) on the 12th of December 2025