LEACE: Perfect linear concept erasure in closed form

• URL: http://arxiv.org/abs/2306.03819v3
• Date: Sun, 29 Oct 2023 21:41:46 GMT
• Title: LEACE: Perfect linear concept erasure in closed form
• Authors: Nora Belrose, David Schneider-Joseph, Shauli Ravfogel, Ryan Cotterell, Edward Raff, Stella Biderman
• Abstract summary: Concept erasure aims to remove specified features from a representation. We introduce LEAst-squares Concept Erasure (LEACE), a closed-form method which provably prevents all linear classifiers from detecting a concept while changing the representation as little as possible. 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.
• Score: 103.61624393221447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Concept erasure aims to remove specified features from a representation. 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 representation 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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