10 papers
Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation
Luyang Fang, Haoran Lu, Yongkai Chen +2
As machine learning models and datasets continue to grow, developing complex models has become increasingly computationally demanding. Knowledge distillation reduces deployment cos…
Confidence-Aware Automated Assessment of Student-Drawn Scientific Models
Luyang Fang, Yingchuan Zhang, Jongchan Park +3
Student-generated drawings are widely used in science education to assess learners' conceptual understanding in modeling-based tasks aligned with the Next Generation Science Standa…
Multi-Teacher Knowledge Distillation via Teacher-Informed Mixture Priors
Luyang Fang, Yongkai Chen, Jiazhang Cai +2
Knowledge distillation is a powerful method for model compression, enabling the efficient deployment of complex deep learning models (teachers), including large language models. Ho…
NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning
Haoran Lu, Luyang Fang, Wenxuan Zhong +1
Multi-agent language systems are often built as hand-designed workflows, where agents are assigned semantic roles and communication protocols are specified in advance. We propose N…
Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions
Luyang Fang, Xiaowei Yu, Jiazhang Cai +23
The exponential growth of Large Language Models (LLMs) continues to highlight the need for efficient strategies to meet ever-expanding computational and data demands. This survey p…
Generalizable and Efficient Automated Scoring with a Knowledge-Distilled Multi-Task Mixture-of-Experts
Luyang Fang, Tao Wang, Ping Ma +1
Automated scoring of written constructed responses typically relies on separate models per task, straining computational resources, storage, and maintenance in real-world education…