Erasing Conceptual Knowledge from Language Models

Rohit Gandikota, Sheridan Feucht, Samuel Marks, David Bau

Keywords: Concept Unlearning, Erasing Bio Threat, Erasing Cyber Threat, safety

TL;DR: This paper introduces ELM, a method to erase concepts from language models using the model's own knowledge classification. It applies targeted updates to reduce concept generation while preserving overall abilities and resisting attacks.

Abstract: In this work, we introduce Erasure of Language Memory (ELM), a principled approach to concept-level unlearning that operates by matching distributions defined by the model's own introspective classification capabilities. Our key insight is that effective unlearning should leverage the model's ability to evaluate its own knowledge, using the language model itself as a classifier to identify and reduce the likelihood of generating content related to undesired concepts. ELM applies this framework to create targeted low-rank updates that reduce generation probabilities for concept-specific content while preserving the model's broader capabilities. We demonstrate ELM's efficacy on biosecurity, cybersecurity, and literary domain erasure tasks. Comparative evaluation reveals that ELM-modified models achieve near-random performance on assessments targeting erased concepts, while simultaneously preserving generation coherence, maintaining benchmark performance on unrelated tasks, and exhibiting strong robustness to adversarial attacks.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 1403