Erasing Conceptual Knowledge from Language Models
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
6701828c797050b1acefdd4c
Rohit Gandikota
admin_assigned
2024-10-07T08:47:27.903Z
636daf1b56c0762cfda074b5
/avatars/f44be5eb110acfa2efbd09de6b416239.svg
Rohit Gandikota
user
RohitGandikota
6701828c797050b1acefdd4d
Sheridan Feucht
admin_assigned
2024-10-07T08:47:34.143Z
653bf2002c81c4adc8f99d70
/avatars/0bd74739cef2df5d4889f108de17a120.svg
Sheridan Feucht
user
sfeucht
6701828c797050b1acefdd4e
Samuel Marks
6701828c797050b1acefdd4f
David Bau
Erasing Conceptual Knowledge from Language Models
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
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.
concept erasure language models evaluation framework innocence seamlessness specificity Erasure of Language Memory (ELM) targeted low-rank updates biosecurity cybersecurity literary domain adversarial attacks