4 citations · 4 across the 6 of their papers we have counts for
6 papers
Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training
Chengqian Zhang, Wei Zhu, Kyumin Lee
Post-training has become essential for adapting large language models (LLMs) to complex downstream behaviors, including instruction following, preference alignment, and multi-step…
From Critique to Clarity: A Pathway to Faithful and Personalized Code Explanations with Large Language Models
Zexing Xu, Zhuang Luo, Yichuan Li +2
In the realm of software development, providing accurate and personalized code explanations is crucial for both technical professionals and business stakeholders. Technical profess…
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…
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…