SSG: Logit-Balanced Vocabulary Partitioning for LLM Watermarking
Watermarking has emerged as a promising technique for tracing the authorship of content generated by large language models (LLMs). Among existing approaches, the KGW scheme is particularly attractive due to its versatility, efficiency, and effectiveness in natural language generation. However, KGW's effectiveness degrades significantly under low-entropy settings such as code generation and mathematical reasoning. A crucial step in the KGW method is random vocabulary partitioning, which enables adjustments to token selection based on specific preferences. Our study revealed that the next-token probability distribution plays an critical role in determining how much, or even whether, we can ...
| Authors | Chenxi Gu, Xiaoning Du, John Grundy |
|---|---|
| Primary category | cs.CR |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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