52 citations · 77 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs
Yichuan Li, Kaize Ding, Kyumin Lee
Self-supervised representation learning on text-attributed graphs, which aims to create expressive and generalizable representations for various downstream tasks, has received incr…
KEPLET: Knowledge-Enhanced Pretrained Language Model with Topic Entity Awareness
Yichuan Li, Jialong Han, Kyumin Lee +3
In recent years, Pre-trained Language Models (PLMs) have shown their superiority by pre-training on unstructured text corpus and then fine-tuning on downstream tasks. On entity-ric…
Fact-Enhanced Synthetic News Generation
Kai Shu, Yichuan Li, Kaize Ding +1
The advanced text generation methods have witnessed great success in text summarization, language translation, and synthetic news generation. However, these techniques can be abuse…