Watermarking of language model outputs enables statistical detection of
model-generated text, which has many applications in the responsible deployment
of language models. Existing watermarking strategies operate by altering the
decoder of an existing language model, and the ability for a language model to
directly learn to generate the watermark would have significant implications
for the real-world deployment of watermarks. First, learned watermarks could be
used to build open models that naturally generate watermarked text, allowing
for open models to benefit from watermarking. Second, if watermarking is used
to determine the provenance of generated text, an adversary can hurt the
reputation of a victim model by spoofing its watermark and generating damaging
watermarked text. To investigate the learnability of watermarks, we propose
watermark distillation, which trains a student model to behave like a teacher
model that uses decoding-based watermarking. We test our approach on three
distinct decoding-based watermarking strategies and various hyperparameter
settings, finding that models can learn to generate watermarked text with high
detectability. We also find limitations to learnability, including the loss of
watermarking capabilities under fine-tuning on normal text and high sample
complexity when learning low-distortion watermarks.

通过水印技术对语言模型生成的文本进行统计检测，有助于在语言模型的负责部署中应用，文章提出了水印蒸馏的方法，通过训练学生模型模仿使用解码器水印技术的教师模型，研究发现模型能够学会生成高可检测性的水印文本，但也存在一些限制。