22 citations · 54 across the 36 of their papers we have counts for
5 papers · 1 filter
Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering
Yuan Sui, Yufei He, Zifeng Ding +1
Recent works integrating Knowledge Graphs (KGs) have shown promising improvements in enhancing the reasoning capabilities of Large Language Models (LLMs). However, existing benchma…
Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training
Yufei He, Zhenyu Hou, Yukuo Cen +5
Graph pre-training has been concentrated on graph-level tasks involving small graphs (e.g., molecular graphs) or learning node representations on a fixed graph. Extending graph pre…
FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering
Yuan Sui, Yufei He, Nian Liu +3
Large Language Models (LLMs) are often challenged by generating erroneous or hallucinated responses, especially in complex reasoning tasks. Leveraging Knowledge Graphs (KGs) as ext…
UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs
Yufei He, Yuan Sui, Xiaoxin He +1
Foundation models like ChatGPT and GPT-4 have revolutionized artificial intelligence, exhibiting remarkable abilities to generalize across a wide array of tasks and applications be…
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Xiaoxin He, Yijun Tian, Yifei Sun +5
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface. In response to a user…