3 citations · 3 across the 3 of their papers we have counts for
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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.AI2023★ 3 cited
TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent System
Haoyuan Li, Hao Jiang, Tianke Zhang +6
Training AI models has always been challenging, especially when there is a need for custom models to provide personalized services. Algorithm engineers often face a lengthy process…