30 citations · 31 across the 5 of their papers we have counts for
5 papers
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…
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…
Efficient Multi-task Prompt Tuning for Recommendation
Ting Bai, Le Huang, Yue Yu +4
With the expansion of business scenarios, real recommender systems are facing challenges in dealing with the constantly emerging new tasks in multi-task learning frameworks. In thi…
Learning Social Graph for Inactive User Recommendation
Nian Liu, Shen Fan, Ting Bai +6
Social relations have been widely incorporated into recommender systems to alleviate data sparsity problem. However, raw social relations don't always benefit recommendation due to…
Graph Foundation Models: Concepts, Opportunities and Challenges
Jiawei Liu, Cheng Yang, Zhiyuan Lu +8
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and seve…