15 citations · 20 across the 14 of their papers we have counts for
14 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
DAR: Deontic Reasoning with Agentic Harnesses
Guangyao Dou, William Jurayj, Nils Holzenberger +1
Deontic reasoning is the task of answering questions by applying explicit rules and policies to case-specific facts, for example computing tax liability under a statute or determin…
DeonticBench: A Benchmark for Reasoning over Rules
Guangyao Dou, Luis Brena, Akhil Deo +4
Reasoning with complex, context-specific rules remains challenging for large language models (LLMs). In legal and policy settings, this manifests as deontic reasoning: reasoning ab…
Steering Multimodal Large Language Models Decoding for Context-Aware Safety
Zheyuan Liu, Zhangchen Xu, Guangyao Dou +4
Multimodal Large Language Models (MLLMs) are increasingly deployed in real-world applications, yet their ability to make context-aware safety decisions remains limited. Existing me…
Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language Models
Zheyuan Liu, Guangyao Dou, Xiangchi Yuan +3
Generative models such as Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) trained on massive datasets can lead them to memorize and inadvertently reveal s…
Investigating the Feasibility of Mitigating Potential Copyright Infringement via Large Language Model Unlearning
Guangyao Dou
Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but also pose risks by learning and generating copyrighted material, leading to significant legal…
Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
Zheyuan Liu, Guangyao Dou, Mengzhao Jia +4
Generative models such as Large Language Models (LLM) and Multimodal Large Language models (MLLMs) trained on massive web corpora can memorize and disclose individuals' confidentia…
Machine Unlearning in Generative AI: A Survey
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan +2
Generative AI technologies have been deployed in many places, such as (multimodal) large language models and vision generative models. Their remarkable performance should be attrib…
Avoiding Copyright Infringement via Large Language Model Unlearning
Guangyao Dou, Zheyuan Liu, Qing Lyu +2
Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but also pose risks by learning and generating copyrighted material, leading to significant legal…
Towards Safer Large Language Models through Machine Unlearning
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan +2
The rapid advancement of Large Language Models (LLMs) has demonstrated their vast potential across various domains, attributed to their extensive pretraining knowledge and exceptio…
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
Zheyuan Liu, Guangyao Dou, Yijun Tian +3
Machine Unlearning (MU) algorithms have become increasingly critical due to the imperative adherence to data privacy regulations. The primary objective of MU is to erase the influe…
EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis
Guangyao Dou, Zheng Zhou
Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals has been a fast-developing area in Brain-Computer Interfaces (BCI). Ho…
BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students
Zheng Zhou, Guangyao Dou, Xiaodong Qu
Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals has been a fast-developing area in Brain-Computer Interfaces (BCI). Ho…
Time Majority Voting, a PC-based EEG Classifier for Non-expert Users
Guangyao Dou, Zheng Zhou, Xiaodong Qu
Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals is a rapidly advancing field in Brain-Computer Interfaces (BCI). In co…