Erasing Conceptual Knowledge from Language Models
Authors: name: Rohit Gandikota, name: Sheridan Feucht, name: Samuel Marks, name: David Bau

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