  In the rapidly evolving domain of artificial intelligence, safeguarding the
intellectual property of Large Language Models (LLMs) is increasingly crucial.
Current watermarking techniques against model extraction attacks, which rely on
signal insertion in model logits or post-processing of generated text, remain
largely heuristic. We propose a novel method for embedding learnable linguistic
watermarks in LLMs, aimed at tracing and preventing model extraction attacks.
Our approach subtly modifies the LLM's output distribution by introducing
controlled noise into token frequency distributions, embedding an statistically
identifiable controllable watermark.We leverage statistical hypothesis testing
and information theory, particularly focusing on Kullback-Leibler Divergence,
to differentiate between original and modified distributions effectively. Our
watermarking method strikes a delicate well balance between robustness and
output quality, maintaining low false positive/negative rates and preserving
the LLM's original performance.
