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arXiv · 2306.03819

LEACE: Perfect linear concept erasure in closed form

Nora Belrose·David Schneider-Joseph·Shauli Ravfogel·Ryan Cotterell·Edward Raff·Stella Biderman
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Abstract

Concept erasure aims to remove specified features from an embedding. It can improve fairness (e.g. preventing a classifier from using gender or race) and interpretability (e.g. removing a concept to observe changes in model behavior). We introduce LEAst-squares Concept Erasure (LEACE), a closed-form method which provably prevents all linear classifiers from detecting a concept while changing the embedding as little as possible, as measured by a broad class of norms. We apply LEACE to large language models with a novel procedure called "concept scrubbing," which erases target concept information from every layer in the network. We demonstrate our method on two tasks: measuring the reliance of language models on part-of-speech information, and reducing gender bias in BERT embeddings. Code is available at https://github.com/EleutherAI/concept-erasure.

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Nora Belrose, David Schneider-Joseph, Shauli Ravfogel, Ryan Cotterell, Edward Raff, Stella Biderman. 2023-06-06. LEACE: Perfect linear concept erasure in closed form. https://arxiv.org/abs/2306.03819

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