4 papers
All You Need is One: Capsule Prompt Tuning with a Single Vector
Yiyang Liu, James C. Liang, Heng Fan +7
Prompt-based learning has emerged as a parameter-efficient finetuning (PEFT) approach to facilitate Large Language Model (LLM) adaptation to downstream tasks by conditioning genera…
Factor Decorrelation Enhanced Data Removal from Deep Predictive Models
Wenhao Yang, Lin Li, Xiaohui Tao +1
The imperative of user privacy protection and regulatory compliance necessitates sensitive data removal in model training, yet this process often induces distributional shifts that…
Cream of the Crop: Harvesting Rich, Scalable and Transferable Multi-Modal Data for Instruction Fine-Tuning
Mengyao Lyu, Yan Li, Huasong Zhong +5
The hypothesis that pretrained large language models (LLMs) necessitate only minimal supervision during the fine-tuning (SFT) stage (Zhou et al., 2024) has been substantiated by re…
Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics
Taowen Wang, Cheng Han, James Chenhao Liang +6
Recently in robotics, Vision-Language-Action (VLA) models have emerged as a transformative approach, enabling robots to execute complex tasks by integrating visual and linguistic i…