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Provable Robust Watermarking for AI-Generated Text (2306.17439v2)

Published 30 Jun 2023 in cs.CL and cs.LG

Abstract: We study the problem of watermarking LLMs generated text -- one of the most promising approaches for addressing the safety challenges of LLM usage. In this paper, we propose a rigorous theoretical framework to quantify the effectiveness and robustness of LLM watermarks. We propose a robust and high-quality watermark method, Unigram-Watermark, by extending an existing approach with a simplified fixed grouping strategy. We prove that our watermark method enjoys guaranteed generation quality, correctness in watermark detection, and is robust against text editing and paraphrasing. Experiments on three varying LLMs and two datasets verify that our Unigram-Watermark achieves superior detection accuracy and comparable generation quality in perplexity, thus promoting the responsible use of LLMs. Code is available at https://github.com/XuandongZhao/Unigram-Watermark.

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Provable Robust Watermarking for AI-Generated Text

The paper "Provable Robust Watermarking for AI-Generated Text" presents a sophisticated framework for watermarking text generated by LLMs. This research is driven by the necessity to identify and verify AI-generated text, addressing safety concerns and potential misuse. The authors introduce the Unigram-Watermark method, which builds on existing watermarking strategies by enhancing robustness against text editing and paraphrasing while maintaining high-quality text generation.

Core Contributions

  1. Theoretical Framework: The paper offers a robust theoretical framework to evaluate the effectiveness of watermarks in AI-generated text. It emphasizes a precise definition of performance, correctness, and resilience against post-processing manipulations, thereby addressing potential vulnerabilities.
  2. Unigram-Watermark Method: The authors present a new watermarking method, the Unigram-Watermark, which extends and refines prior techniques. This method uses a uniform fixed grouping of the vocabulary into a 'green list' and a 'red list,' enhancing resilience against common alterations like synonyms replacement or text paraphrasing. The method ensures that watermarked text remains statistically close to un-watermarked text with bounded Renyi-divergence for all orders.
  3. Experiments and Results: Comprehensive experiments using three LLMs and two datasets illustrate the superior detection accuracy and robustness of the Unigram-Watermark. The experiments confirm the method's high detection accuracy and improved text generation quality, quantified via perplexity scores, without significant degradation.

Key Findings

  • Numerical Results: Empirical findings reveal that the Unigram-Watermark achieves a detection accuracy that surpasses previous watermarking techniques while maintaining comparable text generation quality. Specifically, the perplexity scores of watermarked texts remain close to that of un-watermarked texts, mitigating concerns about quality degradation.
  • Robustness to Edits: The Unigram-Watermark's proof of robustness against arbitrary edits highlights its resilience. With provable guarantees, it can withstand a specified number of text edits without compromising watermark detection.
  • Generalizability: The robustness and efficiency of Unigram-Watermark suggest that its advantages might extend to improving security practices for detecting AI-generated texts beyond its primary design contexts, particularly in areas involving high-stakes manipulations, such as legal document generation and educational assessments.

Implications and Future Directions

The introduction of robust watermark techniques like Unigram-Watermark signifies progress in the field of AI ethics and safety, creating pathways for more secure interactions with AI text generation systems. This technology could become instrumental in mitigating risks associated with fraudulent AI uses, safeguarding intellectual property, and fostering trust in public AI outputs.

Future Research: Further research could investigate cryptographically secure watermarking methods to complement the statistically robust frameworks presented. The challenge of balancing robustness against attack and maintaining low watermark learnability presents a compelling research avenue. Moreover, exploring adaptive watermark strategies that dynamically respond to the evolving techniques used to attack watermark systems could offer enhanced security outcomes.

This work offers a comprehensive solution for embedding and detecting watermarks in AI-generated text, thereby supporting responsible AI usage. It advances theoretical understanding while offering practical tools to reinforce security in LLM outputs, setting a foundation for responsible AI evolution.

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Authors (4)
  1. Xuandong Zhao (47 papers)
  2. Prabhanjan Ananth (28 papers)
  3. Lei Li (1293 papers)
  4. Yu-Xiang Wang (124 papers)
Citations (122)
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  1. GitHub - XuandongZhao/Unigram-Watermark: [ICLR 2024] Provable Robust Watermarking for AI-Generated Text (25 stars)
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