Watermarking Language Models Through Language Models

Summary

Researchers introduce a prompt-guided watermarking framework for LLM outputs that works at the input level and needs no access to model parameters or decoding logits. It uses three cooperating LMs: a prompting LM that writes watermarking instructions, a marking LM that generates watermarked outputs, and a detecting LM that classifies whether a response carries a watermark. Across 25 prompting and marking LM combinations, including GPT-4o, Mistral, LLaMA3 and DeepSeek, the signals generalize across architectures and remain robust under fine-tuning, distillation and prompt-based adversarial attacks.