  Large Language Models (LLMs) have demonstrated impressive capabilities in
generating diverse and contextually rich text. However, concerns regarding
copyright infringement arise as LLMs may inadvertently produce copyrighted
material. In this paper, we first investigate the effectiveness of watermarking
LLMs as a deterrent against the generation of copyrighted texts. Through
theoretical analysis and empirical evaluation, we demonstrate that
incorporating watermarks into LLMs significantly reduces the likelihood of
generating copyrighted content, thereby addressing a critical concern in the
deployment of LLMs. Additionally, we explore the impact of watermarking on
Membership Inference Attacks (MIAs), which aim to discern whether a sample was
part of the pretraining dataset and may be used to detect copyright violations.
Surprisingly, we find that watermarking adversely affects the success rate of
MIAs, complicating the task of detecting copyrighted text in the pretraining
dataset. Finally, we propose an adaptive technique to improve the success rate
of a recent MIA under watermarking. Our findings underscore the importance of
developing adaptive methods to study critical problems in LLMs with potential
legal implications.
