  Potential harms of large language models can be mitigated by watermarking
model output, i.e., embedding signals into generated text that are invisible to
humans but algorithmically detectable from a short span of tokens. We propose a
watermarking framework for proprietary language models. The watermark can be
embedded with negligible impact on text quality, and can be detected using an
efficient open-source algorithm without access to the language model API or
parameters. The watermark works by selecting a randomized set of "green" tokens
before a word is generated, and then softly promoting use of green tokens
during sampling. We propose a statistical test for detecting the watermark with
interpretable p-values, and derive an information-theoretic framework for
analyzing the sensitivity of the watermark. We test the watermark using a
multi-billion parameter model from the Open Pretrained Transformer (OPT)
family, and discuss robustness and security.
