Principled Nonlinear Concept Erasure and Steering
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
Concept erasure aims to remove a target concept from a representation while preserving the other information encoded in it. This is difficult because representations encode many concepts that are often correlated with the erasure target, so removing the target risks damaging them.
arXiv:2607. 03973v1 Announce Type: new Abstract: Concept erasure aims to remove a target concept from a representation while preserving the other information encoded in it.
BARRIER (Bounded Activation Regions for Robust Information Erasure) is a method for machine unlearning that confines parameter updates to a controlled activation space, allowing stronger erasure of targeted concepts while limiting collateral damage to other representations. By employing interval arithmetic, it derives a closed‑form bound on worst‑case representation changes in protected regions, which serves as a knowledge‑preservation objective. The approach is architecture‑agnostic, compatible with existing erasure objectives, and empirically shows competitive performance in both classification and generative tasks, with enhanced robustness against adversarial recovery attacks.
Reference-Guided Machine Unlearning (ReGUn) is a vision unlearning framework that prioritizes distributional indistinguishability over degradation-based heuristics. It uses disjoint held-out data to create a class-conditioned reference distribution for distillation, guiding forget samples toward non-member behavior without explicitly degrading predictions. Experiments across various architectures and datasets show that ReGUn achieves a competitive forgetting–utility trade-off and closely matches retrain-like membership inference behavior.
The paper introduces MUtE, a dual framework that simultaneously erases concept-specific information from representations and generates counterfactual mappings. By deriving new erasure functions based on optimal bounds, MUtE imposes a translational bias on counterfactual trajectories, aligning with geometric properties of concepts in language models. The authors demonstrate that this approach improves downstream algorithmic fairness and enables the generation of counterfactual texts.
The paper introduces DPVar, a fairness metric that measures the variance of the conditional mean prediction to directly optimize mean demographic parity in nonlinear regression. It presents two solvers—FBO, using a closed‑form hypergradient, and ITD, which differentiates through the conditional‑mean estimator updates—to handle the resulting functional bilevel problem. Experiments on 21 tabular datasets and the Communities & Crime data show that these methods achieve competitive or superior accuracy‑fairness trade‑offs compared to existing approaches.