4 citations · 6 across the 9 of their papers we have counts for
9 papers
DELIA: Diversity-Enhanced Learning for Instruction Adaptation in Large Language Models
Yuanhao Zeng, Fei Ren, Xinpeng Zhou +2
Although instruction tuning is widely used to adjust behavior in Large Language Models (LLMs), extensive empirical evidence and research indicates that it is primarily a process wh…
SpanGNN: Towards Memory-Efficient Graph Neural Networks via Spanning Subgraph Training
Xizhi Gu, Hongzheng Li, Shihong Gao +3
Graph Neural Networks (GNNs) have superior capability in learning graph data. Full-graph GNN training generally has high accuracy, however, it suffers from large peak memory usage…
Token-Efficient Leverage Learning in Large Language Models
Yuanhao Zeng, Min Wang, Yihang Wang +1
Large Language Models (LLMs) have excelled in various tasks but perform better in high-resource scenarios, which presents challenges in low-resource scenarios. Data scarcity and th…
LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model
Linmei Hu, Hongyu He, Duokang Wang +3
Personality detection aims to detect one's personality traits underlying in social media posts. One challenge of this task is the scarcity of ground-truth personality traits which…
Dynamic Fair Federated Learning Based on Reinforcement Learning
Weikang Chen, Junping Du, Yingxia Shao +2
Federated learning enables a collaborative training and optimization of global models among a group of devices without sharing local data samples. However, the heterogeneity of dat…
ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems
Jinqing Lian, Xinyi Zhang, Yingxia Shao +4
The past decade has seen rapid growth of distributed stream data processing systems. Under these systems, a stream application is realized as a Directed Acyclic Graph (DAG) of oper…