Multi-Bit Distortion-Free Watermarking for Large Language Models
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929256767946752 |
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| author | Boroujeny, Massieh Kordi Jiang, Ya Zeng, Kai Mark, Brian |
| author_facet | Boroujeny, Massieh Kordi Jiang, Ya Zeng, Kai Mark, Brian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_16578 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Bit Distortion-Free Watermarking for Large Language Models Boroujeny, Massieh Kordi Jiang, Ya Zeng, Kai Mark, Brian Computation and Language Machine Learning 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. |
| title | Multi-Bit Distortion-Free Watermarking for Large Language Models |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2402.16578 |