Avoiding Copyright Infringement via Machine Unlearning

This scenario involves unlearning specific books over time, followed by subsequent Large Language Models (LLMs) (Brown et al., unlearning requests. An effective algorithm 2020; Chowdhery et al., 2023; Touvron et al., 2023) should be stable, meaning it should ensure unlearning have made significant progress through pre-training efficacy--removing unwanted knowledge effectively--while on extensive transformer-based architectures and maintaining locality, preserving learning from diverse text data (Ouyang et al., 2022; non-targeted knowledge and the model's reasoning Kojima et al., 2022; Qin et al., 2023; Lewkowycz ability. Few works have studied this setting, et al., 2022; Roziere et al., 2023; Lyu et al., 2023; leaving it unclear if existing methods are suitable.