6 papers
Neighbor-Aware Localized Concept Erasure in Text-to-Image Diffusion Models
Zhuan Shi, Alireza Dehghanpour Farashah, Rik de Vries +1
Concept erasure in text-to-image diffusion models seeks to remove undesired concepts while preserving overall generative capability. Localized erasure methods aim to restrict edits…
Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs
Alireza Dehghanpour Farashah, Aditi Khandelwal, Marylou Fauchard +3
As multilingual large language models become more widely used, ensuring their safety and fairness across diverse linguistic contexts presents unique challenges. While existing rese…
Reviving Your MNEME: Predicting The Side Effects of LLM Unlearning and Fine-Tuning via Sparse Model Diffing
Aly M. Kassem, Zhuan Shi, Negar Rostamzadeh +1
Large language models (LLMs) are frequently fine-tuned or unlearned to adapt to new tasks or eliminate undesirable behaviors. While existing evaluation methods assess performance a…
FedCDC: A Collaborative Framework for Data Consumers in Federated Learning Market
Zhuan Shi, Patrick Ohl, Boi Faltings
Federated learning (FL) allows machine learning models to be trained on distributed datasets without directly accessing local data. In FL markets, numerous Data Consumers compete t…
CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models
Shunchang Liu, Zhuan Shi, Lingjuan Lyu +2
Assessing whether AI-generated images are substantially similar to source works is a crucial step in resolving copyright disputes. In this paper, we propose CopyJudge, a novel auto…
Copyright-Aware Incentive Scheme for Generative Art Models Using Hierarchical Reinforcement Learning
Zhuan Shi, Yifei Song, Xiaoli Tang +2
Generative art using Diffusion models has achieved remarkable performance in image generation and text-to-image tasks. However, the increasing demand for training data in generativ…