21 citations · 21 across the 3 of their papers we have counts for
3 papers
cs.AI2024
Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework
Jiang Liu, Bolin Li, Haoyuan Li +13
Efficient multimodal large language models (EMLLMs), in contrast to multimodal large language models (MLLMs), reduce model size and computational costs and are often deployed on re…
cs.LG2023★ 21 cited
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning
Yun Zhu, Yaoke Wang, Haizhou Shi +3
Graph-structured data is ubiquitous in the world which models complex relationships between objects, enabling various Web applications. Daily influxes of unlabeled graph data on th…
cs.CL2022
Distilling Task-specific Logical Rules from Large Pre-trained Models
Tao Chen, Luxin Liu, Xuepeng Jia +3
Logical rules, both transferable and explainable, are widely used as weakly supervised signals for many downstream tasks such as named entity tagging. To reduce the human effort of…