Certified Mitigation of Worst-Case LLM Copyright Infringement
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.
68119c70e8a3493171fadce2
Jingyu Zhang
claimed_verified
2025-04-30T07:55:55.481Z
62fb40b59af1d16bc0ac60f4
/avatars/03ff66a419db8f2bc8e89a3b47aaaeac.svg
Jack Zhang
user
jackzhang
68119c70e8a3493171fadce3
Jiacan Yu
68119c70e8a3493171fadce4
Marc Marone
admin_assigned
2025-04-30T12:21:59.233Z
63e410e88083f19a218be964
/avatars/ce271d1686f6e5ee1f5b2d429cb60de6.svg
Marc Marone
user
mmarone
68119c70e8a3493171fadce5
Benjamin Van Durme
68119c70e8a3493171fadce6
Daniel Khashabi
admin_assigned
2025-04-30T12:22:11.443Z
5f6540c65e78cc6b0ed3199d
/avatars/0280d4df417855965a0964d22766c012.svg
Daniel Khashabi
user
danyaljj
Certified Mitigation of Worst-Case LLM Copyright Infringement
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.
BloomScrub, an inference-time method using Bloom filters, effectively reduces copyright infringement risks in large language models by detecting and rewriting infringing quotes.
large language models copyright infringement copyright takedown quote detection rewriting techniques Bloom filters inference-time method certified risk reduction adaptive abstention