k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text
Abe Hou, Jingyu Zhang, Yichen Wang, Daniel Khashabi, Tianxing He
Abstract
Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection. While token-level watermarks are vulnerable to paraphrase attacks, SemStamp (Hou et al., 2023) applies watermark on the semantic representation of sentences and demonstrates promising robustness. SemStamp employs locality-sensitive hashing (LSH) to partition the semantic space with arbitrary hyperplanes, which results in a suboptimal tradeoff between robustness and speed. We propose k-SemStamp, a simple yet effective enhancement of SemStamp, utilizing k-means clustering as an alternative of LSH to partition the embedding space with awareness of inherent semantic structure. Experimental results indicate that k-SemStamp saliently improves its robustness and sampling efficiency while preserving the generation quality, advancing a more effective tool for machine-generated text detection.- Anthology ID:
- 2024.findings-acl.98
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1706–1715
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-acl.98/
- DOI:
- 10.18653/v1/2024.findings-acl.98
- Cite (ACL):
- Abe Hou, Jingyu Zhang, Yichen Wang, Daniel Khashabi, and Tianxing He. 2024. k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text. In Findings of the Association for Computational Linguistics: ACL 2024, pages 1706–1715, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text (Hou et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-acl.98.pdf