2 papers
cs.LG2026
Rethinking LLM Ensembling from the Perspective of Mixture Models
Jiale Fu, Yuchu Jiang, Peijun Wu +3
Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the output distributions of multi…
cs.CV2025
SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning
Junkai Chen, Zhijie Deng, Kening Zheng +6
As Multimodal Large Language Models (MLLMs) develop, their potential security issues have become increasingly prominent. Machine Unlearning (MU), as an effective strategy for forge…