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cs.CL2025
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…
cs.DC2025
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…
cs.CV2025
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…