Papers Machine Unlearning
“Machine Unlearning” 태그가 달린 논문 438편 · 필터 해제
RESTOR: Knowledge Recovery through Machine Unlearning
Large language models trained on web-scale corpora can memorize undesirable datapoints such as incorrect facts, copyrighted content or sensitive data. Recently, many machine unlearning algorithms have been proposed that …
Machine UnlearningAttribute-to-Delete: Machine Unlearning via Datamodel Matching
Machine unlearning -- efficiently removing the effect of a small "forget set" of training data on a pre-trained machine learning model -- has recently attracted significant research interest. Despite this interest, howev…
AttributeMachine UnlearningCLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP
Machine unlearning (MU) has gained significant attention as a means to remove specific data from trained models without requiring a full retraining process. While progress has been made in unimodal domains like text and …
image-classificationImage ClassificationMachine UnlearningProtecting Privacy in Multimodal Large Language Models with MLLMU-Bench
Generative models such as Large Language Models (LLM) and Multimodal Large Language models (MLLMs) trained on massive web corpora can memorize and disclose individuals' confidential and private data, raising legal and et…
Language ModelingLanguage ModellingLarge Language ModelMachine Unlearning+1Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate
Machine unlearning has been used to remove unwanted knowledge acquired by large language models (LLMs). In this paper, we examine machine unlearning from an optimization perspective, framing it as a regularized multi-tas…
Machine UnlearningMachine Unlearning using Forgetting Neural Networks
Modern computer systems store vast amounts of personal data, enabling advances in AI and ML but risking user privacy and trust. For privacy reasons, it is desired sometimes for an ML model to forget part of the data it w…
Handwritten Digit RecognitionMachine UnlearningCLEAR: Character Unlearning in Textual and Visual Modalities
Machine Unlearning (MU) is critical for enhancing privacy and security in deep learning models, particularly in large multimodal language models (MLLMs), by removing specific private or hazardous information. While MU ha…
Machine UnlearningCatastrophic Failure of LLM Unlearning via Quantization
Large language models (LLMs) have shown remarkable proficiency in generating text, benefiting from extensive training on vast textual corpora. However, LLMs may also acquire unwanted behaviors from the diverse and sensit…
Machine UnlearningQuantizationScalability of memorization-based machine unlearning
Machine unlearning (MUL) focuses on removing the influence of specific subsets of data (such as noisy, poisoned, or privacy-sensitive data) from pretrained models. MUL methods typically rely on specialized forms of fine-…
Machine UnlearningMemorizationWhen Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
The deployment of large language models (LLMs) like ChatGPT and Gemini has shown their powerful natural language generation capabilities. However, these models can inadvertently learn and retain sensitive information and…
Machine UnlearningRAGRetrievalRetrieval-augmented Generation+1Evaluating Deep Unlearning in Large Language Models
Machine unlearning is a key requirement of many data protection regulations such as GDPR. Prior work on unlearning has mostly considered superficial unlearning tasks where a single or a few related pieces of information …
Machine UnlearningEdge Unlearning is Not "on Edge"! An Adaptive Exact Unlearning System on Resource-Constrained Devices
The right to be forgotten mandates that machine learning models enable the erasure of a data owner's data and information from a trained model. Removing data from the dataset alone is inadequate, as machine learning mode…
Machine UnlearningEfficient Federated Unlearning under Plausible Deniability
Privacy regulations like the GDPR in Europe and the CCPA in the US allow users the right to remove their data ML applications. Machine unlearning addresses this by modifying the ML parameters in order to forget the influ…
Machine UnlearningMUSO: Achieving Exact Machine Unlearning in Over-Parameterized Regimes
Machine unlearning (MU) is to make a well-trained model behave as if it had never been trained on specific data. In today's over-parameterized models, dominated by neural networks, a common approach is to manually relabe…
Machine UnlearningModel OptimizationA Closer Look at Machine Unlearning for Large Language Models
Large language models (LLMs) may memorize sensitive or copyrighted content, raising privacy and legal concerns. Due to the high cost of retraining from scratch, researchers attempt to employ machine unlearning to remove …
DiversityMachine UnlearningSentenceMachine Unlearning in Forgettability Sequence
Machine unlearning (MU) is becoming a promising paradigm to achieve the "right to be forgotten", where the training trace of any chosen data points could be eliminated, while maintaining the model utility on general test…
Machine UnlearningNoise is All You Need: Private Second-Order Convergence of Noisy SGD
Private optimization is a topic of major interest in machine learning, with differentially private stochastic gradient descent (DP-SGD) playing a key role in both theory and practice. Furthermore, DP-SGD is known to be a…
AllMachine UnlearningNegMerge: Consensual Weight Negation for Strong Machine Unlearning
Machine unlearning aims to selectively remove specific knowledge from a model. Current methods, such as task arithmetic, rely on fine-tuning models on the forget set, generating a task vector, and subtracting it from the…
image-classificationImage ClassificationMachine UnlearningNegation+1CodeUnlearn: Amortized Zero-Shot Machine Unlearning in Language Models Using Discrete Concept
Large Language Models (LLMs) offer extensive knowledge across various domains, but they may inadvertently memorize sensitive, unauthorized, or malicious data, such as personal information in the medical and financial sec…
Machine UnlearningWhy Fine-Tuning Struggles with Forgetting in Machine Unlearning? Theoretical Insights and a Remedial Approach
Machine Unlearning has emerged as a significant area of research, focusing on 'removing' specific subsets of data from a trained model. Fine-tuning (FT) methods have become one of the fundamental approaches for approxima…
Machine Unlearning