title: ContextCite: Attributing Model Generation to Context
summary: How do language models use information provided as context when generating a
  response? Can we infer whether a particular generated statement is actually
  grounded in the context, a misinterpretation, or fabricated? To help answer
  these questions, we introduce the problem of context attribution: pinpointing
  the parts of the context (if any) that led a model to generate a particular
  statement. We then present ContextCite, a simple and scalable method for
  context attribution that can be applied on top of any existing language model.
  Finally, we showcase the utility of ContextCite through three applications: (1)
  helping verify generated statements (2) improving response quality by pruning
  the context and (3) detecting poisoning attacks. We provide code for
  ContextCite at https://github.com/MadryLab/context-cite.
paper_authors._id: 66d802899fcb6b494f44536f
paper_authors.name: Benjamin Cohen-Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-04T09:52:18.420Z
paper_authors._id: 639aaf82a4c528850bba2bfe
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/639aaf82a4c528850bba2bfe/nn23r8bsNiOJzVUxAPfo7.png
paper_authors.fullname: Benjamin Cohen-Wang
paper_authors.type: user
paper_authors.user: bencw
paper_authors._id: 66d802899fcb6b494f445370
paper_authors.name: Harshay Shah
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-04T08:04:28.641Z
paper_authors._id: 64b1a4f64dd3e24895daa236
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64b1a4f64dd3e24895daa236/lzlZ4DBOw-YVspNttAEY3.jpeg
paper_authors.fullname: Harshay Shah
paper_authors.type: user
paper_authors.user: harshay
paper_authors._id: 66d802899fcb6b494f445371
paper_authors.name: Kristian Georgiev
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-04T09:52:23.838Z
paper_authors._id: 63b98550efe99543b34cd33d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63b98550efe99543b34cd33d/lsfiSVfQeKOypMh66c8pT.jpeg
paper_authors.fullname: Kristian Georgiev
paper_authors.type: user
paper_authors.user: krisgrg
paper_authors._id: 66d802899fcb6b494f445372
paper_authors.name: Aleksander Madry
paper_title: ContextCite: Attributing Model Generation to Context
paper_summary: How do language models use information provided as context when generating a
  response? Can we infer whether a particular generated statement is actually
  grounded in the context, a misinterpretation, or fabricated? To help answer
  these questions, we introduce the problem of context attribution: pinpointing
  the parts of the context (if any) that led a model to generate a particular
  statement. We then present ContextCite, a simple and scalable method for
  context attribution that can be applied on top of any existing language model.
  Finally, we showcase the utility of ContextCite through three applications: (1)
  helping verify generated statements (2) improving response quality by pruning
  the context and (3) detecting poisoning attacks. We provide code for
  ContextCite at https://github.com/MadryLab/context-cite.
paper_ai_keywords: context attribution ContextCite