Strong Copyright Protection for Language Models via Adaptive Model Fusion

Autor:	Abad, Javier, Donhauser, Konstantin, Pinto, Francesco, Yang, Fanny
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	The risk of language models unintentionally reproducing copyrighted material from their training data has led to the development of various protective measures. In this paper, we propose model fusion as an effective solution to safeguard against copyright infringement. In particular, we introduce Copyright-Protecting Fusion (CP-Fuse), an algorithm that adaptively combines language models to minimize the reproduction of protected materials. CP-Fuse is inspired by the recently proposed Near-Access Free (NAF) framework and additionally incorporates a desirable balancing property that we demonstrate prevents the reproduction of memorized training data. Our results show that CP-Fuse significantly reduces the memorization of copyrighted content while maintaining high-quality text and code generation. Furthermore, we demonstrate how CP-Fuse can be integrated with other techniques for enhanced protection.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.20105 Zobrazit plný text záznamu View this record from Arxiv