title: Three Bricks to Consolidate Watermarks for Large Language Models
summary: The task of discerning between generated and natural texts is increasingly
  challenging. In this context, watermarking emerges as a promising technique for
  ascribing generated text to a specific model. It alters the sampling generation
  process so as to leave an invisible trace in the generated output, facilitating
  later detection. This research consolidates watermarks for large language
  models based on three theoretical and empirical considerations. First, we
  introduce new statistical tests that offer robust theoretical guarantees which
  remain valid even at low false-positive rates (less than 10^{-6}).
  Second, we compare the effectiveness of watermarks using classical benchmarks
  in the field of natural language processing, gaining insights into their
  real-world applicability. Third, we develop advanced detection schemes for
  scenarios where access to the LLM is available, as well as multi-bit
  watermarking.
paper_authors._id: 64c9da66533bbc49aa0d662b
paper_authors.name: Pierre Fernandez
paper_authors.status: admin_assigned
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paper_authors.fullname: Pierre Fernandez
paper_authors.type: user
paper_authors.user: pierrefdz
paper_authors._id: 64c9da66533bbc49aa0d662c
paper_authors.name: Antoine Chaffin
paper_authors.status: claimed_verified
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paper_authors.fullname: Antoine Chaffin
paper_authors.type: user
paper_authors.user: NohTow
paper_authors._id: 64c9da66533bbc49aa0d662d
paper_authors.name: Karim Tit
paper_authors.status: admin_assigned
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paper_authors._id: 6180eaa864badb2ca4712345
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paper_authors.fullname: Karim TIT
paper_authors.type: user
paper_authors.user: karimtito
paper_authors._id: 64c9da66533bbc49aa0d662e
paper_authors.name: Vivien Chappelier
paper_authors._id: 64c9da66533bbc49aa0d662f
paper_authors.name: Teddy Furon
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-04T20:12:03.856Z
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: Three Bricks to Consolidate Watermarks for Large Language Models
paper_summary: The task of discerning between generated and natural texts is increasingly
  challenging. In this context, watermarking emerges as a promising technique for
  ascribing generated text to a specific model. It alters the sampling generation
  process so as to leave an invisible trace in the generated output, facilitating
  later detection. This research consolidates watermarks for large language
  models based on three theoretical and empirical considerations. First, we
  introduce new statistical tests that offer robust theoretical guarantees which
  remain valid even at low false-positive rates (less than 10^{-6}).
  Second, we compare the effectiveness of watermarks using classical benchmarks
  in the field of natural language processing, gaining insights into their
  real-world applicability. Third, we develop advanced detection schemes for
  scenarios where access to the LLM is available, as well as multi-bit
  watermarking.
paper_ai_summary: This research develops robust watermarking techniques for large language models, introducing statistical tests, comparing watermarks through classical benchmarks, and enhancing detection schemes.
paper_ai_keywords: watermarking large language models statistical tests natural language processing multi-bit watermarking