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[2410.02099] A Watermark for Black-Box Language Models
Summary
The paper presents a novel watermarking scheme for black-box language models, enabling detection of model outputs without requiring white-box access, thus enhancing security and integrity in AI applications.
Why It Matters
As the use of large language models (LLMs) expands, ensuring the authenticity of their outputs becomes crucial. This watermarking method addresses the challenge of detecting LLM-generated content without needing full access to the model, which is vital for developers and researchers focused on AI safety and compliance.
Key Takeaways
- Introduces a watermarking technique that operates with black-box access to LLMs.
- Offers a distortion-free property, enhancing the usability of the watermark.
- Demonstrates performance guarantees and potential advantages over existing white-box schemes.
- Facilitates the detection of AI-generated content, addressing concerns in AI ethics.
- Supports chaining or nesting of watermarks for increased security.
Computer Science > Cryptography and Security arXiv:2410.02099 (cs) [Submitted on 2 Oct 2024 (v1), last revised 23 Feb 2026 (this version, v3)] Title:A Watermark for Black-Box Language Models Authors:Dara Bahri, John Wieting View a PDF of the paper titled A Watermark for Black-Box Language Models, by Dara Bahri and 1 other authors View PDF HTML (experimental) Abstract:Watermarking has recently emerged as an effective strategy for detecting the outputs of large language models (LLMs). Most existing schemes require white-box access to the model's next-token probability distribution, which is typically not accessible to downstream users of an LLM API. In this work, we propose a principled watermarking scheme that requires only the ability to sample sequences from the LLM (i.e. black-box access), boasts a distortion-free property, and can be chained or nested using multiple secret keys. We provide performance guarantees, demonstrate how it can be leveraged when white-box access is available, and show when it can outperform existing white-box schemes via comprehensive experiments. Comments: Subjects: Cryptography and Security (cs.CR); Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2410.02099 [cs.CR] (or arXiv:2410.02099v3 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2410.02099 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Dara Bahri [view email] [v1] Wed, 2 Oct 2024 23:39:19 UTC (5,517 KB) [v2] Thu, 3 Apr 20...
