SpARK: An Embarrassingly Simple Sparse Watermarking in LLMs with Enhanced Text Quality

With the widespread adoption of Large Language Models (LLMs), concerns about potential misuse have emerged. To this end, watermarking has been adapted to LLM, enabling a simple and effective way to detect and monitor generated text. However, while the existing methods can differentiate between watermarked and unwatermarked text with high accuracy, they often face a trade-off between the quality of the generated text and the effectiveness of the watermarking process. In this work, we present a novel type of LLM watermark, Sparse Watermark, which aims to mitigate this trade-off by applying watermarks to a small subset of generated tokens distributed across the text. To demonstrate this type of watermark, we introduce SpARK, a Sparse WatermARK method that achieves sparsity by anchoring watermarked tokens to words that have specific Part-of-Speech (POS) tags. Our experimental results demonstrate that the proposed watermarking scheme, albeit embarrassingly simple, is incredibly effective, achieving high detectability while generating text that outperforms previous LLM watermarking methods in quality across various tasks.