16 citations · 39 across the 17 of their papers we have counts for
8 papers · 1 filter
MEGL: Multimodal Explanation-Guided Learning
Yifei Zhang, Tianxu Jiang, Bo Pan +3
Explaining the decision-making processes of Artificial Intelligence (AI) models is crucial for addressing their "black box" nature, particularly in tasks like image classification.…
GraphNarrator: Generating Textual Explanations for Graph Neural Networks
Bo Pan, Zhen Xiong, Guanchen Wu +3
Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. Despi…
TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations
Zheng Zhang, Yuntong Hu, Bo Pan +2
Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum…
GRAG: Graph Retrieval-Augmented Generation
Yuntong Hu, Zhihan Lei, Zheng Zhang +3
Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and, as a result, falls short in handling networked documents which are very popular in…
Structural Disentanglement of Causal and Correlated Concepts
Qilong Zhao, Shiyu Wang, Zeeshan Memon +5
Controllable data generation aims to synthesize data by specifying values for target concepts. Achieving this reliably requires modeling the underlying generative factors and their…
ELAD: Explanation-Guided Large Language Models Active Distillation
Yifei Zhang, Bo Pan, Chen Ling +2
The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences. Traditional di…
Distilling Large Language Models for Text-Attributed Graph Learning
Bo Pan, Zheng Zhang, Yifei Zhang +2
Text-Attributed Graphs (TAGs) are graphs of connected textual documents. Graph models can efficiently learn TAGs, but their training heavily relies on human-annotated labels, which…
Explaining latent representations of generative models with large multimodal models
Mengdan Zhu, Zhenke Liu, Bo Pan +2
Learning interpretable representations of data generative latent factors is an important topic for the development of artificial intelligence. With the rise of the large multimodal…