title: Watermarking Makes Language Models Radioactive
summary: 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.
paper_authors._id: 65dbffe052eca001fd09ea2b
paper_authors.name: Tom Sander
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-26T10:43:41.587Z
paper_authors._id: 6548e467b17b77265dc334cb
paper_authors.avatarUrl: /avatars/55f45f8ee6a4f477657cf0dd8409c533.svg
paper_authors.fullname: Tom Sander
paper_authors.type: user
paper_authors.user: tomsander1998
paper_authors._id: 65dbffe052eca001fd09ea2c
paper_authors.name: Pierre Fernandez
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-26T10:43:49.016Z
paper_authors._id: 616c22e040e2f69baa1c7ae0
paper_authors.avatarUrl: /avatars/d6c00e7d98e5e8a52e99aa7b1a7815ee.svg
paper_authors.fullname: Pierre Fernandez
paper_authors.type: user
paper_authors.user: pierrefdz
paper_authors._id: 65dbffe052eca001fd09ea2d
paper_authors.name: Alain Durmus
paper_authors._id: 65dbffe052eca001fd09ea2e
paper_authors.name: Matthijs Douze
paper_authors._id: 65dbffe052eca001fd09ea2f
paper_authors.name: Teddy Furon
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-04T20:11:54.125Z
paper_authors._id: 670000393ec4cc293d41d92c
paper_authors.avatarUrl: /avatars/b67843e1f7addaf6769c2cba868bf501.svg
paper_authors.fullname: Teddy Furon
paper_authors.type: user
paper_authors.user: tfuron
paper_title: Watermarking Makes Language Models Radioactive
paper_summary: 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.
paper_ai_keywords: watermarking membership inference synthetic instructions fine-tuning p-value