[2404.02138] Topic-Based Watermarks for Large Language Models

Computer Science > Cryptography and Security arXiv:2404.02138 (cs) [Submitted on 2 Apr 2024 (v1), last revised 2 Mar 2026 (this version, v5)] Title:Topic-Based Watermarks for Large Language Models Authors:Alexander Nemecek, Yuzhou Jiang, Erman Ayday View a PDF of the paper titled Topic-Based Watermarks for Large Language Models, by Alexander Nemecek and 2 other authors View PDF HTML (experimental) 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 model training. Watermarking algorithms offer a viable solution by embedding detectable signatures into generated text. However, existing watermarking methods often involve 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 fluency and coherence. Experimental results across multiple LLMs and state-of-the-art benchmarks demonstrate that our method achieves text quality comparable to industry-leading systems and simultaneously improves watermark robustness against paraphrasing and l...