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WaterJudge: Quality-Detection Trade-off when Watermarking Large Language Models (2403.19548v1)

Published 28 Mar 2024 in cs.CL

Abstract: Watermarking generative-AI systems, such as LLMs, has gained considerable interest, driven by their enhanced capabilities across a wide range of tasks. Although current approaches have demonstrated that small, context-dependent shifts in the word distributions can be used to apply and detect watermarks, there has been little work in analyzing the impact that these perturbations have on the quality of generated texts. Balancing high detectability with minimal performance degradation is crucial in terms of selecting the appropriate watermarking setting; therefore this paper proposes a simple analysis framework where comparative assessment, a flexible NLG evaluation framework, is used to assess the quality degradation caused by a particular watermark setting. We demonstrate that our framework provides easy visualization of the quality-detection trade-off of watermark settings, enabling a simple solution to find an LLM watermark operating point that provides a well-balanced performance. This approach is applied to two different summarization systems and a translation system, enabling cross-model analysis for a task, and cross-task analysis.

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Authors (3)
  1. Piotr Molenda (1 paper)
  2. Adian Liusie (20 papers)
  3. Mark J. F. Gales (37 papers)
Citations (4)
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Summary

Analyzing the Trade-off between Detectability and Quality in LLM Watermarking with WaterJudge Framework

Introduction

The necessity for watermarking in LLMs is increasingly recognized due to the potential for misuse in generating disinformation or academic dishonesty. Current strategies employ watermarking to statistically identify LLM-generated texts. Yet, these interventions often compromise the text's quality, leading to a crucial need for a balanced approach to watermarking that maintains text integrity while ensuring detectability. This paper introduces the WaterJudge framework, a novel method for evaluating the trade-off between watermark detectability and quality degradation in LLM-generated texts.

WaterJudge Framework

Soft-Watermarking Scheme

The proposed soft-watermarking scheme modifies the prediction logits to favor a subset of tokens (green list) over others (red list), based on a hash function of the previous token. This biases the model towards generating green-list tokens, facilitating statistical detection of watermarked texts without needing direct access to the model. This approach allows for the dynamic calculation of green and red lists solely with knowledge of the tokenizer and hashing function, suggesting potential for a standardized watermarking system across multiple models.

Zero-shot Comparative Assessment

To evaluate the impact of watermarking on text quality, the WaterJudge framework incorporates a zero-shot comparative assessment. This method leverages instruction-tuned LLMs to compare pairs of watermarked and unwatermarked texts, estimating the average preference for unwatermarked text as a measure of quality degradation. This innovative approach addresses the limitations of conventional metrics like BLEU or ROUGE, which fail to capture the nuanced effects of watermarking on text quality accurately.

Experimental Setup

Models and Tasks

The framework's versatility is demonstrated through its application to two summarization models, BART and Zephyr, and a translation model, mBART, across summarization and translation tasks. The analysis includes various watermarking parameters, assessing their impact on the quality and detectability of watermarked outputs.

Watermarking Methodology

A comprehensive exploration of watermarking settings reveals a clear trade-off between the strength of the watermark and the resultant text quality. This is quantitatively illustrated through detectability metrics and comparative assessment scores, highlighting the utility of WaterJudge in optimizing watermark parameters for minimal quality degradation.

Results

Trade-off Visualization

Graphical representations provide intuitive insights into the balance between watermark detectability and text quality. These findings underscore the dependency of optimal watermarking settings on model characteristics and task requirements.

Comparative Assessment Validation

The correlation between comparative assessment scores and established evaluation frameworks like UniEval and COMET underscores the validity of this approach in capturing quality degradation. This comparative analysis reinforces the framework's potential as a reliable alternative to traditional metrics.

Cross-Model and Cross-Task Transferability

Preliminary results suggest the possibility of transferring watermark settings between tasks and models, indicating the framework's broader applicability. This insight opens avenues for further exploration into predictive models for watermarking performance across diverse LLM applications.

Conclusions

This paper presents WaterJudge, a framework designed to address the critical balance between watermark detectability and the quality of LLM-generated texts. By employing a sophisticated watermarking scheme and introducing the novel use of zero-shot comparative assessment, the framework facilitates nuanced analysis and optimization of watermarking parameters. Notably, the successful application across different models and tasks, combined with the potential for setting transferability, positions WaterJudge as a significant advancement in the field of LLM watermarking research.

Limitations and Ethical Concerns

The reliance on LLMs for comparative assessment raises questions regarding bias and evaluation accuracy, suggesting areas for further refinement. Additionally, the ethical implications of watermark detectability inaccuracies warrant careful consideration to mitigate potential repercussions for falsely accused individuals.

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