title: Erasing Conceptual Knowledge from Language Models
summary: Concept erasure in language models has traditionally lacked a comprehensive
  evaluation framework, leading to incomplete assessments of effectiveness of
  erasure methods. We propose an evaluation paradigm centered on three critical
  criteria: innocence (complete knowledge removal), seamlessness (maintaining
  conditional fluent generation), and specificity (preserving unrelated task
  performance). Our evaluation metrics naturally motivate the development of
  Erasure of Language Memory (ELM), a new method designed to address all three
  dimensions. ELM employs targeted low-rank updates to alter output distributions
  for erased concepts while preserving overall model capabilities including
  fluency when prompted for an erased concept. We demonstrate ELM's efficacy on
  biosecurity, cybersecurity, and literary domain erasure tasks. Comparative
  analysis shows that ELM achieves superior performance across our proposed
  metrics, including near-random scores on erased topic assessments, generation
  fluency, maintained accuracy on unrelated benchmarks, and robustness under
  adversarial attacks. Our code, data, and trained models are available at
  https://elm.baulab.info
paper_authors._id: 6701828c797050b1acefdd4c
paper_authors.name: Rohit Gandikota
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-07T08:47:27.903Z
paper_authors._id: 636daf1b56c0762cfda074b5
paper_authors.avatarUrl: /avatars/f44be5eb110acfa2efbd09de6b416239.svg
paper_authors.fullname: Rohit Gandikota
paper_authors.type: user
paper_authors.user: RohitGandikota
paper_authors._id: 6701828c797050b1acefdd4d
paper_authors.name: Sheridan Feucht
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-07T08:47:34.143Z
paper_authors._id: 653bf2002c81c4adc8f99d70
paper_authors.avatarUrl: /avatars/0bd74739cef2df5d4889f108de17a120.svg
paper_authors.fullname: Sheridan Feucht
paper_authors.type: user
paper_authors.user: sfeucht
paper_authors._id: 6701828c797050b1acefdd4e
paper_authors.name: Samuel Marks
paper_authors._id: 6701828c797050b1acefdd4f
paper_authors.name: David Bau
paper_title: Erasing Conceptual Knowledge from Language Models
paper_summary: Concept erasure in language models has traditionally lacked a comprehensive
  evaluation framework, leading to incomplete assessments of effectiveness of
  erasure methods. We propose an evaluation paradigm centered on three critical
  criteria: innocence (complete knowledge removal), seamlessness (maintaining
  conditional fluent generation), and specificity (preserving unrelated task
  performance). Our evaluation metrics naturally motivate the development of
  Erasure of Language Memory (ELM), a new method designed to address all three
  dimensions. ELM employs targeted low-rank updates to alter output distributions
  for erased concepts while preserving overall model capabilities including
  fluency when prompted for an erased concept. We demonstrate ELM's efficacy on
  biosecurity, cybersecurity, and literary domain erasure tasks. Comparative
  analysis shows that ELM achieves superior performance across our proposed
  metrics, including near-random scores on erased topic assessments, generation
  fluency, maintained accuracy on unrelated benchmarks, and robustness under
  adversarial attacks. Our code, data, and trained models are available at
  https://elm.baulab.info
paper_ai_summary: An evaluation paradigm and method, ELM, for concept erasure in language models addresses effectiveness, fluency, and unrelated performance through targeted low-rank updates, demonstrating superior results in various erasure tasks.
paper_ai_keywords: concept erasure language models evaluation framework innocence seamlessness specificity Erasure of Language Memory (ELM) targeted low-rank updates biosecurity cybersecurity literary domain adversarial attacks