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.

Related papers

• Scaling Concept With Text-Guided Diffusion Models [53.80799139331966]
  Instead of replacing a concept, can we enhance or suppress the concept itself? We introduce ScalingConcept, a simple yet effective method to scale decomposed concepts up or down in real input without introducing new elements. More importantly, ScalingConcept enables a variety of novel zero-shot applications across image and audio domains.
  arXiv  Detail & Related papers  (2024-10-31T17:09:55Z)
• Collapsed Language Models Promote Fairness [88.48232731113306]
  We find that debiased language models exhibit collapsed alignment between token representations and word embeddings. We design a principled fine-tuning method that can effectively improve fairness in a wide range of debiasing methods.
  arXiv  Detail & Related papers  (2024-10-06T13:09:48Z)
• Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts [8.028021897214238]
  "OpenCBM" is the first CBM with concepts of open vocabularies. Our model significantly outperforms the previous state-of-the-art CBM by 9% in the classification accuracy on the benchmark dataset CUB-200-2011.
  arXiv  Detail & Related papers  (2024-08-05T06:42:00Z)
• Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery [52.498055901649025]
  Concept Bottleneck Models (CBMs) have been proposed to address the 'black-box' problem of deep neural networks. We propose a novel CBM approach -- called Discover-then-Name-CBM (DN-CBM) -- that inverts the typical paradigm. Our concept extraction strategy is efficient, since it is agnostic to the downstream task, and uses concepts already known to the model.
  arXiv  Detail & Related papers  (2024-07-19T17:50:11Z)
• Concept Distillation: Leveraging Human-Centered Explanations for Model Improvement [3.026365073195727]
  Concept Activation Vectors (CAVs) estimate a model's sensitivity and possible biases to a given concept. We extend CAVs from post-hoc analysis to ante-hoc training in order to reduce model bias through fine-tuning. We show applications of concept-sensitive training to debias several classification problems.
  arXiv  Detail & Related papers  (2023-11-26T14:00:14Z)
• Implicit Concept Removal of Diffusion Models [92.55152501707995]
  Text-to-image (T2I) diffusion models often inadvertently generate unwanted concepts such as watermarks and unsafe images. We present the Geom-Erasing, a novel concept removal method based on the geometric-driven control.
  arXiv  Detail & Related papers  (2023-10-09T17:13:10Z)
• A Geometric Notion of Causal Probing [91.14470073637236]
  In a language model's representation space, all information about a concept such as verbal number is encoded in a linear subspace. We give a set of intrinsic criteria which characterize an ideal linear concept subspace. We find that LEACE returns a one-dimensional subspace containing roughly half of total concept information.
  arXiv  Detail & Related papers  (2023-07-27T17:57:57Z)
• Probing Classifiers are Unreliable for Concept Removal and Detection [18.25734277357466]
  Neural network models trained on text data have been found to encode undesirable linguistic or sensitive concepts in their representation. Recent work has proposed post-hoc and adversarial methods to remove such unwanted concepts from a model's representation. We show that these methods can be counter-productive, and in the worst case may end up destroying all task-relevant features.
  arXiv  Detail & Related papers  (2022-07-08T23:15:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.