Multi-Bit Distortion-Free Watermarking for Large Language Models
Massieh Kordi Boroujeny Ya Jiang Kai Zeng Brian Mark
Methods for watermarking large language models have been proposed that
distinguish AI-generated text from human-generated text by slightly altering
the model output distribution, but they also distort the quality of the text,
exposing the watermark to adversarial detection. More recently, distortion-free
watermarking methods were proposed that require a secret key to detect the
watermark. The prior methods generally embed zero-bit watermarks that do not
provide additional information beyond tagging a text as being AI-generated. We
extend an existing zero-bit distortion-free watermarking method by embedding
multiple bits of meta-information as part of the watermark. We also develop a
computationally efficient decoder that extracts the embedded information from
the watermark with low bit error rate.