Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data? (2407.17417v1)

Abstract: 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.

Overview of Watermarking Techniques in LLMs for Copyright Protection

The paper "Can Watermarking LLMs Prevent Copyrighted Text Generation and Hide Training Data?" by Panaitescu-Liess et al. investigates the efficacy of watermarking techniques in LLMs to alleviate copyright infringement concerns. The authors systematically explore multiple facets of watermarking, demonstrating its impact not only in preventing the generation of copyrighted texts but also in obfuscating membership inference attacks (MIAs), thereby impeding the identification of copyrighted data in training datasets.

Core Contributions

Prevention of Copyrighted Text Generation

The researchers highlight a significant reduction in the likelihood of generating copyrighted content through the application of watermarking techniques. Two specific methods, UMD and Unigram-Watermark, are examined. Both approaches rely on splitting the vocabulary into two groups (green and red tokens) and biasing the model to preferentially select green tokens during text generation. The empirical results confirm that watermarking can increase the perplexity of generating copyrighted texts, thus making it exponentially less probable for LLMs to produce such material.

For instance, using Llama-30B, the relative increase in perplexity and the reduction in probability often exceeded orders of magnitude. Specifically, the Unigram-Watermark method caused a relative increase of $4.1$ in the minimum and $34.1$ in the average perplexity for training samples, equating to a reduction in generation probability by more than $10^{22}$ times. Such findings are robust across various LLMs and data splits, underscoring that watermarking can effectively mitigate the verbatim reproduction of copyrighted contents.

Watermarking Versus Membership Inference Attacks

While watermarking successfully thwarts the generation of copyrighted text, it simultaneously deteriorates the success rates of MIAs. These attacks attempt to detect whether specific data were included in the model's training set, which is crucial for uncovering copyright violations. The investigation included five LLMs and the WikiMIA benchmark datasets, with results indicating that watermarking can lower the AUC of detection methods by up to $16.4\%$. This reduction in AUC signifies that watermarking negatively impacts the ability of MIAs to accurately discern training data membership.

Adaptive Techniques for Improved Detection

To counter the degradation in MIA success due to watermarking, the authors propose an adaptive version of the Min-K\% Prob attack. This adaptive method recalibrates the model's output probabilities by compensating for the watermark-induced alterations. The empirical evaluation of this adaptive technique demonstrates improvement in detection performance, recovering up to $4.8\%$ in AUC relative to the non-adaptive approach.

Theoretical Insights

The paper also presents theoretical analyses to substantiate the empirical findings. For hard watermarking schemes (UMD), the upper bound on the probability of generating copyrighted content diminishes exponentially with the increase in text length. For soft watermarking schemes, a similar drop in generation probability is achieved, showcased through precise mathematical formulations.

Implications and Future Directions

The research provides a dual perspective on watermarking techniques for LLMs. On one hand, it offers a practical solution for reducing the inadvertent generation of copyrighted texts, crucial for ethical and legal AI deployments. On the other hand, it brings to light the challenge watermarking poses for MIAs aimed at auditing copyright compliance.

Future studies could explore alternative watermarking schemes beyond decoding-time methods and further enhance adaptive MIA techniques to strike a better balance between text generation quality and the ability to detect training data memberships. Investigating watermarking techniques that can both prevent copyright infringements during deployment and support copyright violation audits remains a pivotal area for AI research and development.

In summary, this paper reveals the intricate interplay between preventing the generation of copyrighted content and the complications that watermarking introduces for data privacy and copyright auditing in LLMs. The nuanced understanding provided by this research is essential for formulating comprehensive strategies for copyright protection in the evolving landscape of AI.