title: Certified Mitigation of Worst-Case LLM Copyright Infringement
summary: The exposure of large language models (LLMs) to copyrighted material during
  pre-training raises concerns about unintentional copyright infringement post
  deployment. This has driven the development of "copyright takedown" methods,
  post-training approaches aimed at preventing models from generating content
  substantially similar to copyrighted ones. While current mitigation approaches
  are somewhat effective for average-case risks, we demonstrate that they
  overlook worst-case copyright risks exhibits by the existence of long, verbatim
  quotes from copyrighted sources. We propose BloomScrub, a remarkably simple yet
  highly effective inference-time approach that provides certified copyright
  takedown. Our method repeatedly interleaves quote detection with rewriting
  techniques to transform potentially infringing segments. By leveraging
  efficient data sketches (Bloom filters), our approach enables scalable
  copyright screening even for large-scale real-world corpora. When quotes beyond
  a length threshold cannot be removed, the system can abstain from responding,
  offering certified risk reduction. Experimental results show that BloomScrub
  reduces infringement risk, preserves utility, and accommodates different levels
  of enforcement stringency with adaptive abstention. Our results suggest that
  lightweight, inference-time methods can be surprisingly effective for copyright
  prevention.
paper_authors._id: 68119c70e8a3493171fadce2
paper_authors.name: Jingyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-30T07:55:55.481Z
paper_authors._id: 62fb40b59af1d16bc0ac60f4
paper_authors.avatarUrl: /avatars/03ff66a419db8f2bc8e89a3b47aaaeac.svg
paper_authors.fullname: Jack Zhang
paper_authors.type: user
paper_authors.user: jackzhang
paper_authors._id: 68119c70e8a3493171fadce3
paper_authors.name: Jiacan Yu
paper_authors._id: 68119c70e8a3493171fadce4
paper_authors.name: Marc Marone
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-30T12:21:59.233Z
paper_authors._id: 63e410e88083f19a218be964
paper_authors.avatarUrl: /avatars/ce271d1686f6e5ee1f5b2d429cb60de6.svg
paper_authors.fullname: Marc Marone
paper_authors.type: user
paper_authors.user: mmarone
paper_authors._id: 68119c70e8a3493171fadce5
paper_authors.name: Benjamin Van Durme
paper_authors._id: 68119c70e8a3493171fadce6
paper_authors.name: Daniel Khashabi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-30T12:22:11.443Z
paper_authors._id: 5f6540c65e78cc6b0ed3199d
paper_authors.avatarUrl: /avatars/0280d4df417855965a0964d22766c012.svg
paper_authors.fullname: Daniel Khashabi
paper_authors.type: user
paper_authors.user: danyaljj
paper_title: Certified Mitigation of Worst-Case LLM Copyright Infringement
paper_summary: The exposure of large language models (LLMs) to copyrighted material during
  pre-training raises concerns about unintentional copyright infringement post
  deployment. This has driven the development of "copyright takedown" methods,
  post-training approaches aimed at preventing models from generating content
  substantially similar to copyrighted ones. While current mitigation approaches
  are somewhat effective for average-case risks, we demonstrate that they
  overlook worst-case copyright risks exhibits by the existence of long, verbatim
  quotes from copyrighted sources. We propose BloomScrub, a remarkably simple yet
  highly effective inference-time approach that provides certified copyright
  takedown. Our method repeatedly interleaves quote detection with rewriting
  techniques to transform potentially infringing segments. By leveraging
  efficient data sketches (Bloom filters), our approach enables scalable
  copyright screening even for large-scale real-world corpora. When quotes beyond
  a length threshold cannot be removed, the system can abstain from responding,
  offering certified risk reduction. Experimental results show that BloomScrub
  reduces infringement risk, preserves utility, and accommodates different levels
  of enforcement stringency with adaptive abstention. Our results suggest that
  lightweight, inference-time methods can be surprisingly effective for copyright
  prevention.
paper_ai_summary: BloomScrub, an inference-time method using Bloom filters, effectively reduces copyright infringement risks in large language models by detecting and rewriting infringing quotes.
paper_ai_keywords: large language models copyright infringement copyright takedown quote detection rewriting techniques Bloom filters inference-time method certified risk reduction adaptive abstention