143 citations · 205 across the 16 of their papers we have counts for
7 papers · 1 filter
MemoTime: Memory-Augmented Temporal Knowledge Graph Enhanced Large Language Model Reasoning
Xingyu Tan, Xiaoyang Wang, Qing Liu +4
Large Language Models (LLMs) have achieved impressive reasoning abilities, but struggle with temporal understanding, especially when questions involve multiple entities, compound o…
PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generation
Xiangjun Zai, Xingyu Tan, Xiaoyang Wang +3
Knowledge Hypergraphs (KHs) have recently emerged as a knowledge representation for retrieval-augmented generation (RAG), offering a paradigm to model multi-entity relations into a…
MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs
Yiheng Hu, Xiaoyang Wang, Qing Liu +4
Multimodal Multi-hop question answering requires integrating information from diverse sources, such as images and texts, to derive answers. Existing methods typically rely on seque…
LLM-based HSE Compliance Assessment: Benchmark, Performance, and Advancements
Jianwei Wang, Mengqi Wang, Yinsi Zhou +5
Health, Safety, and Environment (HSE) compliance assessment demands dynamic real-time decision-making under complicated regulations and complex human-machine-environment interactio…
HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning
Xingyu Tan, Xiaoyang Wang, Qing Liu +4
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge. Current hybrid RAG system retrieves evidence from both knowledge gra…
Unlocking Multi-Modal Potentials for Link Prediction on Dynamic Text-Attributed Graphs
Yuanyuan Xu, Wenjie Zhang, Ying Zhang +2
Dynamic Text-Attributed Graphs (DyTAGs) are a novel graph paradigm that captures evolving temporal events (edges) alongside rich textual attributes. Existing studies can be broadly…
On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message Passing
Jianwei Wang, Kai Wang, Ying Zhang +3
Missing data imputation, which aims to impute the missing values in the raw datasets to achieve the completeness of datasets, is crucial for modern data-driven models like large la…