Multi-Bit Distortion-Free Watermarking for Large Language Models

• URL: http://arxiv.org/abs/2402.16578v1
• Date: Mon, 26 Feb 2024 14:01:34 GMT
• Title: Multi-Bit Distortion-Free Watermarking for Large Language Models
• Authors: Massieh Kordi Boroujeny, Ya Jiang, Kai Zeng, Brian Mark
• Abstract summary: 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.
• Score: 4.7381853007029475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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