5 papers
What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning
Yuchang Zhu, Huazhen Zhong, Qunshu Lin +6
With the remarkable generative capabilities of large language models (LLMs), using LLM-generated data to train downstream models has emerged as a promising approach to mitigate dat…
SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding
Yuchang Zhu, Jintang Li, Huizhe Zhang +2
Individual fairness (IF) in graph neural networks (GNNs), which emphasizes the need for similar individuals should receive similar outcomes from GNNs, has been a critical issue. De…
GT-SNT: A Linear-Time Transformer for Large-Scale Graphs via Spiking Node Tokenization
Huizhe Zhang, Jintang Li, Yuchang Zhu +2
Graph Transformers (GTs), which integrate message passing and self-attention mechanisms simultaneously, have achieved promising empirical results in graph prediction tasks. However…
Are Large Language Models In-Context Graph Learners?
Jintang Li, Ruofan Wu, Yuchang Zhu +3
Large language models (LLMs) have demonstrated remarkable in-context reasoning capabilities across a wide range of tasks, particularly with unstructured inputs such as language or…
Measuring Diversity in Synthetic Datasets
Yuchang Zhu, Huizhe Zhang, Bingzhe Wu +5
Large language models (LLMs) are widely adopted to generate synthetic datasets for various natural language processing (NLP) tasks, such as text classification and summarization. H…