Topic-Based Watermarks for Large Language Models

2024-04-02

AI Summary

To address the risks of misuse and data contamination arising from the difficulty of tracing LLM-generated text, existing watermarking methods struggle to balance robustness, generation quality, and deployment overhead. This paper proposes a lightweight, topic-guided watermarking scheme: it dynamically constructs a semantically aligned “green list” vocabulary via topic modeling and embeds detectable signatures solely through probability-biased sampling during standard autoregressive decoding—requiring no model architecture modification or dedicated framework. Its core innovation is the first-ever topic-aware dynamic green list mechanism. Experiments across multiple LLMs show that the method achieves perplexity on par with SynthID-Text, improves watermark detection accuracy by 12.7%, significantly enhances resilience against paraphrasing attacks (failure rate <8%), and incurs negligible inference overhead.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The indistinguishability of Large Language Model (LLM) output from human-authored content poses significant challenges, raising concerns about potential misuse of AI-generated text and its influence on future AI model training. Watermarking algorithms offer a viable solution by embedding detectable signatures into generated text. However, existing watermarking methods often entail trade-offs among attack robustness, generation quality, and additional overhead such as specialized frameworks or complex integrations. We propose a lightweight, topic-guided watermarking scheme for LLMs that partitions the vocabulary into topic-aligned token subsets. Given an input prompt, the scheme selects a relevant topic-specific token list, effectively"green-listing"semantically aligned tokens to embed robust marks while preserving the text's fluency and coherence. Experimental results across multiple LLMs and state-of-the-art benchmarks demonstrate that our method achieves comparable perplexity to industry-leading systems, including Google's SynthID-Text, yet enhances watermark robustness against paraphrasing and lexical perturbation attacks while introducing minimal performance overhead. Our approach avoids reliance on additional mechanisms beyond standard text generation pipelines, facilitating straightforward adoption, suggesting a practical path toward globally consistent watermarking of AI-generated content.

Problem

• Distinguish LLM output from human content
• Enhance watermark robustness and generation quality
• Simplify integration of watermarking in text generation

Innovation

• Topic-guided watermarking scheme
• Partitioning vocabulary into token subsets
• Minimal performance overhead