3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2024
PERFT: Parameter-Efficient Routed Fine-Tuning for Mixture-of-Expert Model
Yilun Liu, Yunpu Ma, Shuo Chen +4
The Mixture-of-Experts (MoE) paradigm has emerged as a powerful approach for scaling transformers with improved resource utilization. However, efficiently fine-tuning MoE models re…
cs.CL2024
A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation
Yongkang Liu, Ercong Nie, Shi Feng +5
Current state-of-the-art dialogue systems heavily rely on extensive training datasets. However, challenges arise in domains where domain-specific training datasets are insufficient…
cs.LG2023★ 3 cited
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning
Zifeng Ding, Jingpei Wu, Zongyue Li +2
Temporal knowledge graph completion (TKGC) aims to predict the missing links among the entities in a temporal knwoledge graph (TKG). Most previous TKGC methods only consider predic…