4 citations · 6 across the 12 of their papers we have counts for
5 papers · 1 filter
CLEAR-KGQA: Clarification-Enhanced Ambiguity Resolution for Knowledge Graph Question Answering
Liqiang Wen, Guanming Xiong, Tong Mo +3
This study addresses the challenge of ambiguity in knowledge graph question answering (KGQA). While recent KGQA systems have made significant progress, particularly with the integr…
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better
Xu Chu, Hanlin Xue, Zhijie Tan +3
The success of Large Language Models (LLMs) in various domains has led researchers to apply them to graph-related problems by converting graph data into natural language text. Howe…
Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions
Zhijie Tan, Yuzhi Li, Shengwei Meng +5
Current popular Large Vision-Language Models (LVLMs) are suffering from Hallucinations on Object Attributes (HoOA), leading to incorrect determination of fine-grained attributes in…
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
Xu Chu, Hanlin Xue, Bingce Wang +5
Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier ed…
Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains
Xu Chu, Zhijie Tan, Hanlin Xue +3
Large Language Models (LLMs) are widely applied to downstream domains. However, current LLMs for high-stakes domain tasks, such as financial investment and legal QA, typically gene…