Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique

arXiv:2510.09655 · cs.CR, cs.AI · Submitted 2025-10-07 · Read on arXiv

cs.CR, cs.AI

Submitted: 2025-10-07

Updated: 2026-09-22

Journal ref: ICML 2026 - Forty-Third International Conference on Machine Learning, Jul 2026, Seoul, South Korea

Code: https://github.com/openlm-research/open_llama

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Provenance — 39× in this paper · explained in 4 episodes
Fine-Tuning — 35× in this paper · explained in 5 episodes
Data Provenance — 27× in this paper · explained in 2 episodes
Verification — 17× in this paper · explained in 2 episodes
Robustness — 11× in this paper · explained in 5 episodes
Tokenization — 7× in this paper · explained in 2 episodes
Adaptation — 3× in this paper · explained in 1 episode
Reinforcement Learning — 3× in this paper · explained in 4 episodes
Symposium on Security and Privacy — 3× in this paper
Adam — 2× in this paper · explained in 2 episodes
Benchmark — 2× in this paper · explained in 2 episodes
Conference on Secure and Trustworthy Machine Learning — 2× in this paper
Generative AI — 2× in this paper · explained in 2 episodes
Transformer — 2× in this paper · explained in 2 episodes
Accuracy — 1× in this paper · explained in 1 episode
Consistency — 1× in this paper · explained in 3 episodes
Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
Distillation — 1× in this paper · explained in 2 episodes
Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
Quantization — 1× in this paper · explained in 12 episodes
Regularization — 1× in this paper · explained in 3 episodes
Trace — 1× in this paper · explained in 2 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes
alpha — 1× in this paper · explained in 1 episode

Sources

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