3 papers
cs.LG2025
GMoE: Empowering LLMs Fine-Tuning via MoE Graph Collaboration
Ting Bai, Yue Yu, Le Huang +2
The sparse Mixture-of-Experts (MoE) architecture of large language models (LLMs) confronts an inherent issue of load imbalance arising from the simplistic linear router strategy, w…
cs.LG2025
Self-Evolving LLMs via Continual Instruction Tuning
Jiazheng Kang, Le Huang, Cheng Hou +3
In real-world industrial settings, large language models (LLMs) must learn continually to keep pace with diverse and evolving tasks, requiring self-evolution to refine knowledge un…
cs.AI2024
Emotional RAG: Enhancing Role-Playing Agents through Emotional Retrieval
Le Huang, Hengzhi Lan, Zijun Sun +2
As LLMs exhibit a high degree of human-like capability, increasing attention has been paid to role-playing research areas in which responses generated by LLMs are expected to mimic…