Strong Copyright Protection for Language Models via Adaptive Model Fusion
This addresses copyright infringement risks for users of language models, though it appears incremental as it builds on existing frameworks.
The paper tackles the problem of language models unintentionally reproducing copyrighted material by proposing Copyright-Protecting Fusion (CP-Fuse), an adaptive model fusion algorithm that significantly reduces memorization of copyrighted content while maintaining high-quality text and code generation.
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.