SAEMark: Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoders
Zhuohao Yu, Xingru Jiang, Weizheng Gu, Yidong Wang, Qingsong Wen, Shikun Zhang, Wei Ye
Watermarking LLM-generated text is critical for content attribution and misinformation prevention.  However, existing methods compromise text quality, require white-box model access and logit manipulation —limitations that exclude API-based models and multilingual / domain-specific use cases.  We propose **SAEMark**, a multi-bit watermarking method that embeds personalized signatures without altering logits.  SAEMark utilizes **Sparse Autoencoder (SAE)** to extract semantic features of generated texts and selects outputs matching watermark key-derived feature distributions via rejection sampling. This approach operates post-generation, preserving text quality while naturally generalizing to multilingual scenarios including programming languages.Experiments across 4 datasets shows SAEMark's consistent accuracy, highest text quality among 9 baselines. SAEMark establishes a new paradigm: secure, personalizable watermarks that work out-of-the-box for all languages and closed-source LLMs for ethical and trustworthy AI systems.