Papers Machine Unlearning
“Machine Unlearning” 태그가 달린 논문 438편 · 필터 해제
Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning
Machine unlearning offers a promising solution to privacy and safety concerns in large language models (LLMs) by selectively removing targeted knowledge while preserving utility. However, current methods are highly sensi…
Machine UnlearningMathSentiment AnalysisSpeech Unlearning
We introduce machine unlearning for speech tasks, a novel and underexplored research problem that aims to efficiently and effectively remove the influence of specific data from trained speech models without full retraini…
Adversarial RobustnessKeyword SpottingMachine UnlearningSpeaker IdentificationAligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race
Although value-aligned language models (LMs) appear unbiased in explicit bias evaluations, they often exhibit stereotypes in implicit word association tasks, raising concerns about their fair usage. We investigate the me…
Machine UnlearningDoes Machine Unlearning Truly Remove Model Knowledge? A Framework for Auditing Unlearning in LLMs
In recent years, Large Language Models (LLMs) have achieved remarkable advancements, drawing significant attention from the research community. Their capabilities are largely attributed to large-scale architectures, whic…
Machine UnlearningTargeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images
Machine unlearning aims to remove the influence of specific training samples from a trained model without full retraining. While prior work has largely focused on privacy-motivated settings, we recast unlearning as a gen…
Bilevel OptimizationMachine UnlearningSoft Weighted Machine Unlearning
Machine unlearning, as a post-hoc processing technique, has gained widespread adoption in addressing challenges like bias mitigation and robustness enhancement, colloquially, machine unlearning for fairness and robustnes…
counterfactualFairnessMachine UnlearningLeveraging Per-Instance Privacy for Machine Unlearning
We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearning (Chien et al., 2024), obtaining a be…
Machine UnlearningUnlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs
Unlearning in large language models (LLMs) is intended to remove the influence of specific data, yet current evaluations rely heavily on token-level metrics such as accuracy and perplexity. We show that these metrics can…
DiagnosticMachine UnlearningMathLosing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value
The proliferation of large models has intensified the need for efficient data valuation methods to quantify the contribution of individual data providers. Traditional approaches, such as game-theory-based Shapley value a…
Data ValuationMachine UnlearningUniErase: Unlearning Token as a Universal Erasure Primitive for Language Models
Large language models require iterative updates to address challenges such as knowledge conflicts and outdated information (e.g., incorrect, private, or illegal contents). Machine unlearning provides a systematic methodo…
Machine UnlearningModel EditingWorld KnowledgeA Unified Gradient-based Framework for Task-agnostic Continual Learning-Unlearning
Recent advancements in deep models have highlighted the need for intelligent systems that combine continual learning (CL) for knowledge acquisition with machine unlearning (MU) for data removal, forming the Continual Lea…
Continual LearningIncremental LearningMachine Unlearning"Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding
Machine unlearning, the process of efficiently removing specific information from machine learning models, is a growing area of interest for responsible AI. However, few studies have explored the effectiveness of unlearn…
Machine UnlearningSpoken Language UnderstandingDUSK: Do Not Unlearn Shared Knowledge
Large language models (LLMs) are increasingly deployed in real-world applications, raising concerns about the unauthorized use of copyrighted or sensitive data. Machine unlearning aims to remove such 'forget' data while …
Machine UnlearningSEPS: A Separability Measure for Robust Unlearning in LLMs
Machine unlearning aims to selectively remove targeted knowledge from Large Language Models (LLMs), ensuring they forget specified content while retaining essential information. Existing unlearning metrics assess whether…
Machine UnlearningFew-Shot Concept Unlearning with Low Rank Adaptation
Image Generation models are a trending topic nowadays, with many people utilizing Artificial Intelligence models in order to generate images. There are many such models which, given a prompt of a text, will generate an i…
DenoisingImage GenerationMachine UnlearningReady2Unlearn: A Learning-Time Approach for Preparing Models with Future Unlearning Readiness
This paper introduces Ready2Unlearn, a learning-time optimization approach designed to facilitate future unlearning processes. Unlike the majority of existing unlearning efforts that focus on designing unlearning algorit…
Machine UnlearningMeta-LearningPrivacy-Aware Lifelong Learning
Lifelong learning algorithms enable models to incrementally acquire new knowledge without forgetting previously learned information. Contrarily, the field of machine unlearning focuses on explicitly forgetting certain pr…
image-classificationImage ClassificationIncremental LearningLifelong learning+2MCU: Improving Machine Unlearning through Mode Connectivity
Machine Unlearning (MU) aims to remove the information of specific training data from a trained model, ensuring compliance with privacy regulations and user requests. While one line of existing MU methods relies on linea…
image-classificationImage ClassificationMachine UnlearningTask ArithmeticTAIJI: MCP-based Multi-Modal Data Analytics on Data Lakes
The variety of data in data lakes presents significant challenges for data analytics, as data scientists must simultaneously analyze multi-modal data, including structured, semi-structured, and unstructured data. While L…
AI AgentMachine UnlearningLayered Unlearning for Adversarial Relearning
Our goal is to understand how post-training methods, such as fine-tuning, alignment, and unlearning, modify language model behavior and representations. We are particularly interested in the brittle nature of these modif…
Language ModelingLanguage ModellingLarge Language ModelMachine Unlearning+1