title: Precise In-Parameter Concept Erasure in Large Language Models
summary: Large language models (LLMs) often acquire knowledge during pretraining that
  is undesirable in downstream deployments, e.g., sensitive information or
  copyrighted content. Existing approaches for removing such knowledge rely on
  fine-tuning, training low-rank adapters or fact-level editing, but these are
  either too coarse, too shallow, or ineffective. In this work, we propose PISCES
  (Precise In-parameter Suppression for Concept EraSure), a novel framework for
  precisely erasing entire concepts from model parameters by directly editing
  directions that encode them in parameter space. PISCES uses a disentangler
  model to decompose MLP vectors into interpretable features, identifies those
  associated with a target concept using automated interpretability techniques,
  and removes them from model parameters. Experiments on Gemma 2 and Llama 3.1
  over various concepts show that PISCES achieves modest gains in efficacy over
  leading erasure methods, reducing accuracy on the target concept to as low as
  7.7%, while dramatically improving erasure specificity (by up to 31%) and
  robustness (by up to 38%). Overall, these results demonstrate that
  feature-based in-parameter editing enables a more precise and reliable approach
  for removing conceptual knowledge in language models.
paper_authors._id: 6838c514570877d07100f318
paper_authors.name: Yoav Gur-Arieh
paper_authors._id: 6838c514570877d07100f319
paper_authors.name: Clara Suslik
paper_authors._id: 6838c514570877d07100f31a
paper_authors.name: Yihuai Hong
paper_authors._id: 6838c514570877d07100f31b
paper_authors.name: Fazl Barez
paper_authors._id: 6838c514570877d07100f31c
paper_authors.name: Mor Geva
paper_title: Precise In-Parameter Concept Erasure in Large Language Models
paper_summary: Large language models (LLMs) often acquire knowledge during pretraining that
  is undesirable in downstream deployments, e.g., sensitive information or
  copyrighted content. Existing approaches for removing such knowledge rely on
  fine-tuning, training low-rank adapters or fact-level editing, but these are
  either too coarse, too shallow, or ineffective. In this work, we propose PISCES
  (Precise In-parameter Suppression for Concept EraSure), a novel framework for
  precisely erasing entire concepts from model parameters by directly editing
  directions that encode them in parameter space. PISCES uses a disentangler
  model to decompose MLP vectors into interpretable features, identifies those
  associated with a target concept using automated interpretability techniques,
  and removes them from model parameters. Experiments on Gemma 2 and Llama 3.1
  over various concepts show that PISCES achieves modest gains in efficacy over
  leading erasure methods, reducing accuracy on the target concept to as low as
  7.7%, while dramatically improving erasure specificity (by up to 31%) and
  robustness (by up to 38%). Overall, these results demonstrate that
  feature-based in-parameter editing enables a more precise and reliable approach
  for removing conceptual knowledge in language models.
paper_ai_summary: PISCES, a novel framework using feature-based in-parameter editing, effectively erases concepts from large language models with high specificity and robustness.
paper_ai_keywords: PISCES Precise In-parameter Suppression for Concept EraSure MLP vectors disentangler model automated interpretability techniques feature-based in-parameter editing