Scalable watermarking for identifying large language model outputs
Nature volume 634, pages 818–823 (2024 )Cite this article
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 ecosystem1,2,3. Watermarking can help identify synthetic text and limit accidental or deliberate misuse4, 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 systems5. 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 Gemini6 responses, again confirming the preservation of text quality. We hope that the availability of SynthID-Text7 will facilitate further development of watermarking and responsible use of LLM systems.
Large language models (LLMs) are widely adopted tools for synthetic text generation, finding applications in language-based assistants, code generation, writing support and various other domains. As LLMs advance in quality, coherence, coverage and expertise, it can become difficult to distinguish synthetically generated text from human-written text1,2,3. Given the widespread use of LLMs in education, software development and web content generation, identification and attribution of LLM text is critical to ensure safe and responsible use of the technology8,9,10,11.
