LEACE: Perfect linear concept erasure in closed form
Concept erasure aims to remove specified features from a representation. It
can be used to 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). In this paper, we introduce LEAst-squares Concept Erasure
(LEACE), a closed-form method which provably prevents all linear classifiers
from detecting a concept while inflicting the least possible damage to the
representation. 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 the usefulness of 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.
647fdcc800bb4708d24964b6
Nora Belrose
claimed_verified
2023-06-07T19:40:31.162Z
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Nora Belrose
user
norabelrose
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David Schneider-Joseph
647fdcc800bb4708d24964b8
Shauli Ravfogel
claimed_verified
2023-06-07T19:40:23.449Z
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Shauli Ravfogel
user
ravfogs
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Ryan Cotterell
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Edward Raff
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Stella Biderman
extracted_confirmed
2024-02-13T05:23:36.561Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/60347d3660e3dd96631c9093/B3fuZer5N04tZIAYrLnz4.jpeg
Stella Biderman
user
stellaathena
LEACE: Perfect linear concept erasure in closed form
Concept erasure aims to remove specified features from a representation. It
can be used to 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). In this paper, we introduce LEAst-squares Concept Erasure
(LEACE), a closed-form method which provably prevents all linear classifiers
from detecting a concept while inflicting the least possible damage to the
representation. 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 the usefulness of 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.
LEACE is a method to remove specific features from large language models via "concept scrubbing," enhancing fairness and interpretability.
concept erasure LEAST-squares Concept Erasure LEACE linear classifiers concept scrubbing part-of-speech information gender bias BERT embeddings