52 citations · 77 across the 11 of their papers we have counts for
4 papers · 1 filter
Let's Ask GNN: Empowering Large Language Model for Graph In-Context Learning
Zhengyu Hu, Yichuan Li, Zhengyu Chen +4
Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap…
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification
Guanyi Mou, Yichuan Li, Kyumin Lee
Data augmentation has shown its effectiveness in resolving the data-hungry problem and improving model's generalization ability. However, the quality of augmented data can be varie…
Empowering Large Language Models for Textual Data Augmentation
Yichuan Li, Kaize Ding, Jianling Wang +1
With the capabilities of understanding and executing natural language instructions, Large language models (LLMs) can potentially act as a powerful tool for textual data augmentatio…
MEND: Meta dEmonstratioN Distillation for Efficient and Effective In-Context Learning
Yichuan Li, Xiyao Ma, Sixing Lu +3
Large Language models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities, where a LLM makes predictions for a given test input together with a few input-out…