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Preserving Utility in Continual LLM Unlearning via Orthogonal Projections

Paper recorded by Signals 4 on 2026-10-29 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.LG · 机器学习 · first seen 2026-10-30

Abstract

Unlearning concerns the task of removing the influence of a sensitive portion of the training data, such as protected, copyrighted or private text, from a model without retraining it from scratch. While unlearning requests happen continuously during a model's lifetime, the vast majority of LLM unlearning methods are ill-suited for iterated removal, yielding steep decay in forgetting and utility up

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#82 most recent of 703 cs.LG papers we have recorded · ↑ newer: The Precision Wall: An Optimizer-Dependent Boundary for Optimization u · ↓ older: Cheap Retrieval and Observation Credit Assignment from One Quantity: T
Cite this page: Preserving Utility in Continual LLM Unlearning via Orthogonal Projections: the #82 most recent of 703 cs.LG papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/preserving-utility-in-continual-llm-unlearning-via-orthogonal-projections.html
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