SAEMark: Steering Personalized Multilingual LLM Watermarks with Sparse Autoencoders

TL;DR

SAEMark introduces a post-hoc, feature-based watermarking method for multilingual LLMs that preserves text quality, works without model access or training, and achieves high detection accuracy across multiple datasets.

Contribution

It proposes a novel, inference-time watermarking framework using feature-based rejection sampling with Sparse Autoencoders, enabling scalable, multilingual, and high-quality watermarking without model modification.

Findings

Achieves 99.7% F1 score on English datasets.

Demonstrates effective multi-bit detection accuracy across 4 datasets.

Provides theoretical guarantees relating success probability and compute budget.

Abstract

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. These limitations exclude API-based models and multilingual scenarios. We propose SAEMark, a general framework for post-hoc multi-bit watermarking that embeds personalized messages solely via inference-time, feature-based rejection sampling without altering model logits or requiring training. Our approach operates on deterministic features extracted from generated text, selecting outputs whose feature statistics align with key-derived targets. This framework naturally generalizes across languages and domains while preserving text quality through sampling LLM outputs instead of modifying. We provide theoretical guarantees relating watermark success probability and compute budget that hold for any suitable feature extractor. Empirically, we demonstrate the framework's effectiveness using Sparse Autoencoders (SAEs), achieving superior detection accuracy and text quality. Experiments across 4 datasets show SAEMark's consistent performance, with 99.7% F1 on English and strong multi-bit detection accuracy. SAEMark establishes a new paradigm for scalable watermarking that works out-of-the-box with closed-source LLMs while enabling content attribution.