Forging LLM Authorship Fingerprints with Targeted Rewriting
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
arXiv:2501. 15509v5 Announce Type: replace-cross Abstract: Model fingerprinting has emerged as a crucial mechanism for safeguarding the intellectual property of open-source models, offering a non-intrusive approach that requires no modifications to the protected model.
arXiv:2604. 01904v3 Announce Type: replace-cross Abstract: Post-hoc unauthorized-training data detection for large language models (LLMs) typically assumes a query-with-originals regime: rights holders query a target LLM with raw proprietary data and assess whether the model assigns them stronger memorization-based detection signals, e.
The study investigates whether provenance information can reliably identify the source of synthetic text and whether this identification improves the selection of training data. Using financial‑risk text, the authors achieve 98.7% accuracy in attributing original generated passages, but accuracy drops to 53.1% after paraphrasing and 29.0% after style rewriting. They compare two selection strategies—one based on source provenance and another on a reference model score—across three rounds of generation and retraining, finding that the two methods choose different examples but do not produce a consistent difference in model degradation. The results suggest that source attribution and useful data selection are distinct challenges, and neither provenance nor the tested proxy suffices to guarantee stable recursive training behavior.
arXiv:2506. 14003v5 Announce Type: replace Abstract: Machine unlearning (MU) for large language models (LLMs), commonly referred to as LLM unlearning, seeks to remove specific undesirable data or knowledge from a trained model, while maintaining its performance on standard tasks.
arXiv:2606. 10794v1 Announce Type: new Abstract: As agentic applications increasingly route user tasks through official and third-party LLM APIs, provenance becomes an operational question: which model generated a given black-box response?
arXiv:2607. 29539v1 Announce Type: cross Abstract: Standard AI-text detection benchmarks compare human-written text against text generated directly by large language models (LLMs).