Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?

TL;DR

This paper explores watermarking techniques in large language models to prevent copyright infringement, analyzing their effectiveness and impact on privacy attacks, and proposing adaptive methods to improve detection under watermarking.

Contribution

It provides a theoretical and empirical evaluation of watermarking in LLMs, revealing its effects on copyright protection and membership inference attacks, and introduces an adaptive technique to enhance attack success.

Findings

Watermarking reduces copyrighted text generation in LLMs.

Watermarking hampers the effectiveness of membership inference attacks.

Adaptive methods can improve attack success rates under watermarking.

Abstract

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. However, we also find that watermarking can have unintended consequences 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. These results reveal the complex interplay between different regulatory measures, which may impact each other in unforeseen ways. 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.