Scalable watermarking for identifying large language model outputs
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Presents SynthID-Text, a practical watermarking technique for identifying LLM-generated text in production systems, addressing AI safety concerns around detecting synthetic content and potential misuse of generated text.
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This paper introduces SynthID-Text, a production-ready watermarking scheme for identifying text generated by large language models. The method preserves text quality while enabling high detection accuracy with minimal computational overhead, requiring only modifications to the sampling procedure without affecting model training. The authors demonstrate the scheme's effectiveness through evaluations across multiple LLMs and a large-scale live experiment with nearly 20 million Gemini responses, showing improved detectability compared to existing methods while maintaining text quality and model capabilities.
Cited by 2 pages
| Page | Type | Quality |
|---|---|---|
| AI Content Authentication | Approach | 58.0 |
| AI Model Steganography | Risk | 91.0 |
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Scalable watermarking for identifying large language model outputs | Nature
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Scalable watermarking for identifying large language model outputs
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Computer science
Information technology
Abstract
Large language models (LLMs) have enabled the generation of high-quality synthetic text, often indistinguishable from human-written content, at a scale that can markedly affect the nature of the information ecosystem 1 , 2 , 3 . Watermarking can help identify synthetic text and limit accidental or deliberate misuse 4 , but has not been adopted in production systems owing to stringent quality, detectability and computational efficiency requirements. Here we describe SynthID-Text, a production-ready text watermarking scheme that preserves text quality and enables high detection accuracy, with minimal latency overhead. SynthID-Text does not affect LLM training and modifies only the sampling procedure; watermark detection is computationally efficient, without using the underlying LLM. To enable watermarking at scale, we develop an algorithm integrating watermarking with speculative sampling, an efficiency technique frequently used in production systems 5 . Evaluations across multiple LLMs empirically show that SynthID-Text provides improved detectability over comparable methods, and standard benchmarks and human side-by-side ratings indicate no change in LLM capabilities. To demonstrate the feasibility of watermarking in large-scale-production systems, we conducted a live experiment that assessed feedback from nearly 20 million Gemini 6 responses, again confirming the preservation of text quality. We hope that the availability of SynthID-Text 7 will facilitate further development of watermarking and responsible use of LLM systems.
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