Multi-Bit Distortion-Free Watermarking for Large Language Models
<|reference_start|>Multi-Bit Distortion-Free Watermarking for Large Language Models: 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.<|reference_end|>
@article{boroujeny2024multi-bit,
    title={Multi-Bit Distortion-Free Watermarking for Large Language Models},
    author={Massieh Kordi Boroujeny, Ya Jiang, Kai Zeng, Brian Mark},
    journal={arXiv preprint arXiv:2402.16578},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16578},
    primaryClass={cs.CL cs.LG}
}