We propose a methodology for planting watermarks in text from an
autoregressive language model that are robust to perturbations without
changing the distribution over text up to a certain maximum generation budget.
We generate watermarked text by mapping a sequence of random numbers—which
we compute using a randomized watermark key—to a sample from the language model.
To detect watermarked text, any party who knows the key can align the
text to the random number sequence.
We instantiate our watermark methodology with two sampling schemes:
inverse transform sampling and exponential minimum sampling.
We apply these watermarks to three language models—OPT-1.3B, LLaMA-7B and Alpaca-7B—to
experimentally validate their statistical power and robustness to various paraphrasing attacks.
Notably, for both the OPT-1.3B and LLaMA-7B models, we find we can reliably detect watermarked text ()  fromtokens
even after corrupting between-% of the tokens via random edits (i.e., substitutions,
insertions or deletions) .
For the Alpaca-7B model, we conduct a case study on the feasibility of watermarking responses
to typical user instructions. Due to the lower entropy of the responses, detection is more difficult:
aroundof the responses—whose median length is aroundtokens—are detectable with,
and the watermark is also less robust to certain automated paraphrasing attacks we implement.111We release all code publicly athttps://github.com/jthickstun/watermark.