Strong Copyright Protection for Language Models via Adaptive Model Fusion
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Code GenerationMemorizationSimilar Papers 제목 키워드 기반
On Provable Copyright Protection for Generative Models
There is a growing concern that learned conditional generative models may output samples that are substantially similar to some copyrighted data $C$ that was in their training set. We give a formal definition of $\textit…
RLCP: A Reinforcement Learning-based Copyright Protection Method for Text-to-Image Diffusion Model
The increasing sophistication of text-to-image generative models has led to complex challenges in defining and enforcing copyright infringement criteria and protection. Existing methods, such as watermarking and dataset …
Decision MakingDenoisingCPR: Retrieval Augmented Generation for Copyright Protection
Retrieval Augmented Generation (RAG) is emerging as a flexible and robust technique to adapt models to private users data without training, to handle credit attribution, and to allow efficient machine unlearning at scale…
Image GenerationMachine UnlearningRAGRetrieval+1CopyrightMeter: Revisiting Copyright Protection in Text-to-image Models
Text-to-image diffusion models have emerged as powerful tools for generating high-quality images from textual descriptions. However, their increasing popularity has raised significant copyright concerns, as these models …
Image GenerationText to Image GenerationText-to-Image GenerationBeyond English: Unveiling Multilingual Bias in LLM Copyright Compliance
Large Language Models (LLMs) have raised significant concerns regarding the fair use of copyright-protected content. While prior studies have examined the extent to which LLMs reproduce copyrighted materials, they have p…