23 citations · 39 across the 6 of their papers we have counts for
7 papers
A Practical Method for Generating String Counterfactuals
Matan Avitan, Ryan Cotterell, Yoav Goldberg +1
Interventions targeting the representation space of language models (LMs) have emerged as an effective means to influence model behavior. Such methods are employed, for example, to…
Representation Surgery: Theory and Practice of Affine Steering
Shashwat Singh, Shauli Ravfogel, Jonathan Herzig +3
Language models often exhibit undesirable behavior, e.g., generating toxic or gender-biased text. In the case of neural language models, an encoding of the undesirable behavior is…
LEACE: Perfect linear concept erasure in closed form
Nora Belrose, David Schneider-Joseph, Shauli Ravfogel +3
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. rem…
Log-linear Guardedness and its Implications
Shauli Ravfogel, Yoav Goldberg, Ryan Cotterell
Methods for erasing human-interpretable concepts from neural representations that assume linearity have been found to be tractable and useful. However, the impact of this removal o…
Visual Comparison of Language Model Adaptation
Rita Sevastjanova, Eren Cakmak, Shauli Ravfogel +2
Neural language models are widely used; however, their model parameters often need to be adapted to the specific domains and tasks of an application, which is time- and resource-co…
Kernelized Concept Erasure
Shauli Ravfogel, Francisco Vargas, Yoav Goldberg +1
The representation space of neural models for textual data emerges in an unsupervised manner during training. Understanding how those representations encode human-interpretable con…
Linear Adversarial Concept Erasure
Shauli Ravfogel, Michael Twiton, Yoav Goldberg +1
Modern neural models trained on textual data rely on pre-trained representations that emerge without direct supervision. As these representations are increasingly being used in rea…