Publicly-Detectable Watermarking for Language Models

• URL: http://arxiv.org/abs/2310.18491v4
• Date: Sat, 04 Jan 2025 13:52:49 GMT
• Title: Publicly-Detectable Watermarking for Language Models
• Authors: Jaiden Fairoze, Sanjam Garg, Somesh Jha, Saeed Mahloujifar, Mohammad Mahmoody, Mingyuan Wang,
• Abstract summary: We present a publicly-detectable watermarking scheme for LMs. We embed a cryptographic signature into LM output using rejection sampling. We prove that this produces unforgeable and distortion-free text output.
• Score: 45.32236917886154
• License:
• Abstract: We present a publicly-detectable watermarking scheme for LMs: the detection algorithm contains no secret information, and it is executable by anyone. We embed a publicly-verifiable cryptographic signature into LM output using rejection sampling and prove that this produces unforgeable and distortion-free (i.e., undetectable without access to the public key) text output. We make use of error-correction to overcome periods of low entropy, a barrier for all prior watermarking schemes. We implement our scheme and find that our formal claims are met in practice.

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