Advancing Beyond Identification: Multi-bit Watermark for Large Language Models

KiYoon Yoo, Wonhyuk Ahn, Nojun Kwak


Abstract
We show the viability of tackling misuses of large language models beyond the identification of machine-generated text. While existing zero-bit watermark methods focus on detection only, some malicious misuses demand tracing the adversary user for counteracting them. To address this, we propose Multi-bit Watermark via Position Allocation, embedding traceable multi-bit information during language model generation. Through allocating tokens onto different parts of the messages, we embed longer messages in high corruption settings without added latency. By independently embedding sub-units of messages, the proposed method outperforms the existing works in terms of robustness and latency. Leveraging the benefits of zero-bit watermarking, our method enables robust extraction of the watermark without any model access, embedding and extraction of long messages ( 32-bit) without finetuning, and maintaining text quality, while allowing zero-bit detection all at the same time.
Anthology ID:
2024.naacl-long.224
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4031–4055
Language:
URL:
https://aclanthology.org/2024.naacl-long.224
DOI:
Bibkey:
Cite (ACL):
KiYoon Yoo, Wonhyuk Ahn, and Nojun Kwak. 2024. Advancing Beyond Identification: Multi-bit Watermark for Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4031–4055, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Advancing Beyond Identification: Multi-bit Watermark for Large Language Models (Yoo et al., NAACL 2024)
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https://aclanthology.org/2024.naacl-long.224.pdf
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 2024.naacl-long.224.copyright.pdf