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LEACE: Perfect linear concept erasure in closed form

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arxiv 2306.03819 v4 pith:YQLGT5C3 submitted 2023-06-06 cs.LG cs.CLcs.CY

Nora Belrose , David Schneider-Joseph , Shauli Ravfogel , Ryan Cotterell , Edward Raff , Stella Biderman This is my paper
classification cs.LGcs.CLcs.CY
keywords concepterasureleaceembeddinggenderinformationlanguagelinear
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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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Cited by 5 Pith papers

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