Precise In-Parameter Concept Erasure in Large Language Models
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.
6838c514570877d07100f318
Yoav Gur-Arieh
6838c514570877d07100f319
Clara Suslik
6838c514570877d07100f31a
Yihuai Hong
6838c514570877d07100f31b
Fazl Barez
6838c514570877d07100f31c
Mor Geva
Precise In-Parameter Concept Erasure in Large Language Models
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.
PISCES, a novel framework using feature-based in-parameter editing, effectively erases concepts from large language models with high specificity and robustness.
PISCES Precise In-parameter Suppression for Concept EraSure MLP vectors disentangler model automated interpretability techniques feature-based in-parameter editing