7 papers
Per-parameter Task Arithmetic for Unlearning in Large Language Models
Chengyi Cai, Zesheng Ye, Jiangchao Yao +5
In large language model (LLM) unlearning, private information is required to be removed. Task arithmetic unlearns by subtracting a specific task vector (TV)--defined as the paramet…
Visual-Guided Key-Token Regularization for Multimodal Large Language Model Unlearning
Chengyi Cai, Zesheng Ye, Peike Li +3
Unlearning in Multimodal Large Language Models (MLLMs) prevents the model from revealing private information when queried about target images. Existing MLLM unlearning methods larg…
ENTP: Enhancing Low-Quality SFT Data via Neural-Symbolic Text Purge-Mix
Zile Yang, Ling Li, Na Di +5
Supervised Fine-Tuning (SFT) adapts pre-trained Large Language Models (LLMs) to domain-specific instructions by training on a carefully curated subset of high-quality instruction-r…
Lang-PINN: From Language to Physics-Informed Neural Networks via a Multi-Agent Framework
Xin He, Liangliang You, Hongduan Tian +3
Physics-informed neural networks (PINNs) provide a powerful approach for solving partial differential equations (PDEs), but constructing a usable PINN remains labor-intensive and e…
Understanding and Enhancing the Transferability of Jailbreaking Attacks
Runqi Lin, Bo Han, Fengwang Li +1
Jailbreaking attacks can effectively manipulate open-source large language models (LLMs) to produce harmful responses. However, these attacks exhibit limited transferability, faili…
Transferability of Adversarial Attacks in Video-based MLLMs: A Cross-modal Image-to-Video Approach
Linhao Huang, Xue Jiang, Zhiqiang Wang +5
Video-based multimodal large language models (V-MLLMs) have shown vulnerability to adversarial examples in video-text multimodal tasks. However, the transferability of adversarial…