Position: Iterative Online-Offline Joint Optimization Is Needed to Manage Complex LLM Copyright Risks
Abstract
The infringement risks of LLMs have raised significant copyright concerns across different stages of the model lifecycle. While current methods often address these issues separately, this position paper argues that the LLM copyright challenges are inherently connected, and independent optimization of these solutions leads to theoretical bottlenecks. Building on this insight, we further argue that managing LLM copyright risks requires a systemic approach rather than fragmented solutions. In this paper, we analyze the limitations of existing methods in detail and introduce an iterative online-offline joint optimization framework to effectively manage complex LLM copyright risks. We demonstrate that this framework offers a scalable and practical solution to mitigate LLM infringement risks, and also outline new research directions that emerge from this perspective.
Cite
Text
Pan et al. "Position: Iterative Online-Offline Joint Optimization Is Needed to Manage Complex LLM Copyright Risks." Proceedings of the 42nd International Conference on Machine Learning, 2025.Markdown
[Pan et al. "Position: Iterative Online-Offline Joint Optimization Is Needed to Manage Complex LLM Copyright Risks." Proceedings of the 42nd International Conference on Machine Learning, 2025.](https://mlanthology.org/icml/2025/pan2025icml-position/)BibTeX
@inproceedings{pan2025icml-position,
title = {{Position: Iterative Online-Offline Joint Optimization Is Needed to Manage Complex LLM Copyright Risks}},
author = {Pan, Yanzhou and Chen, Jiayi and Chen, Jiamin and Xu, Zhaozhuo and Zhang, Denghui},
booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
year = {2025},
pages = {81962-81976},
volume = {267},
url = {https://mlanthology.org/icml/2025/pan2025icml-position/}
}