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:
- 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)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.naacl-long.224.pdf