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.CL2024
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition
Tianwei Lin, Jiang Liu, Wenqiao Zhang +9
While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA have effectively addressed GPU memory constraints during fine-tuning, their performance often falls short, especially…
cs.CV2024
MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis
Wanggui He, Siming Fu, Mushui Liu +10
Auto-regressive models have made significant progress in the realm of language generation, yet they do not perform on par with diffusion models in the domain of image synthesis. In…