Rethinking Evaluation Methods for Machine Unlearning

Machine *unlearning* refers to methods for deleting information about specific training instances from a trained machine learning model. This ...

Rethinking Evaluation Methods for Machine Unlearning. Leon Wichert. Leibniz University Hannover [email protected]. Sandipan Sikdar. L3S ...

Rethinking Machine Unlearning for Large Language Models - arXiv

Accordingly, it becomes challenging to precisely define and localize the 'unlearning targets', such as the subset of the training set or a ...

Rethinking Machine Unlearning for Large Language Models - arXiv

We navigate the unlearning landscape in LLMs from conceptual formulation, methodologies, metrics, and applications. In particular, we highlight ...

[PDF] Rethinking Machine Unlearning for Large Language Models

This initiative aims to eliminate undesirable data influence and the associated model capabilities, while maintaining the integrity of essential knowledge ...

Research - Google Sites

Leon Wichert and Sandipan Sikdar. Rethinking Evaluation Methods for Machine Unlearning. Findings of EMNLP 2024, Miami, Florida. Seham Nasr and ...

tamlhp/awesome-machine-unlearning - GitHub

Model-agnostic machine unlearning methodologies include unlearning processes or frameworks that are applicable for different models. In some cases, they provide ...

Mohit Bansal on X: " Important + fun collaboration on this new ...

Important + fun collaboration on this new position paper ➡➡ "Rethinking Machine Unlearning for Large Language Models ... assessment ▪ ...

Rethinking Machine Unlearning for Large Language Models - Linnk AI

This involves leveraging knowledge and insights gained from unlearning in language models and applying them to other modalities. Evaluation Metrics: Develop new ...

3 Recommendations for Machine Unlearning Evaluation Challenges

Machine unlearning (MU) aims to develop methods to remove data points efficiently and effectively from a model without the need for ...

(PDF) Revisiting Machine Unlearning with Dimensional Alignment

Based on these findings, we introduce a novel evaluation metric for machine unlearning, coined dimensional alignment, which measures the ...

Revisiting Machine Unlearning for Large Language Models

... evaluation with four NLP datasets as well as a case study on real-world datasets, our methods consistently show superiority over the first-order methods.

Existing Literature about Machine Unlearning - GitHub

Liu et al. Rethinking Machine Unlearning for Large Language Models, arXiv ... Romandini et al. Federated Unlearning: A Survey on Methods, Design Guidelines, and ...

Towards Unbounded Machine Unlearning - OpenReview

Evaluating inexact unlearning requires revisiting forgetting. arXiv preprint ... Descent-to-delete: Gradient-based methods for machine unlearning. In ...

Rethinking Machine Unlearning for Large Language Models

To address the issue of inadequate evaluation of model outputs after unlearning, we introduce three additional metrics to evaluate token ...

Rethinking Entity-level Unlearning for Large Language Models

In response, this paper introduces a novel class of machine unlearning algorithms. First method is partial amnesiac unlearning, integration of layer-wise ...

MUSE: Machine Unlearning Six-Way Evaluation for Language Models

1 Introduction. Training language models (LMs) often involves using vast amounts of text data, which may inadver- · 2 Machine Unlearning: ...

Machine Unlearning Catching a Wave - LinkedIn

Traditional machine learning methods and techniques do not work effectively on defining the scope of unlearning, optimizing for re-training cost ...

Machine Unlearning in Large Language Models - AIModels.fyi

Rethinking Machine Unlearning in Large Language Models proposes a new unlearning method that focuses on removing information related to ...

Rethinking Adversarial Robustness in the Context of the Right to be...

... machine learning models to unlearn a fraction of training data and its lineage. As a result of this growing interest, numerous machine unlearning methods ...