Watermarking Makes Language Models Radioactive
Tom Sander Pierre Fernandez Alain Durmus Matthijs Douze Teddy Furon
This paper investigates the radioactivity of LLM-generated texts, i.e.
whether it is possible to detect that such input was used as training data.
Conventional methods like membership inference can carry out this detection
with some level of accuracy. We show that watermarked training data leaves
traces easier to detect and much more reliable than membership inference. We
link the contamination level to the watermark robustness, its proportion in the
training set, and the fine-tuning process. We notably demonstrate that training
on watermarked synthetic instructions can be detected with high confidence
(p-value < 1e-5) even when as little as 5% of training text is watermarked.
Thus, LLM watermarking, originally designed for detecting machine-generated
text, gives the ability to easily identify if the outputs of a watermarked LLM
were used to fine-tune another LLM.