76 citations · 117 across the 25 of their papers we have counts for
11 papers · 1 filter
VizRAG: Enhancing Retrieval-Augmented Generation with Hypergraph Visualization
Yanbin Wei, Yang Chen, Renling Gan +7
Hypergraph-based RAG systems surpass traditional graph-based approaches by organizing complex n-ary atomic facts among entities, rather than relying solely on binary relationships.…
HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning
Yanbin Wei, Chun Kang, Siwei Li +11
Large Vision-Language Models (LVLMs) consistently require new arenas to guide their expanding boundaries, yet their capabilities with hypergraphs remain unexplored. In the real wor…
DocPruner: A Storage-Efficient Framework for Multi-Vector Visual Document Retrieval via Adaptive Patch-Level Embedding Pruning
Yibo Yan, Guangwei Xu, Xin Zou +3
Visual Document Retrieval (VDR), the task of retrieving visually-rich document pages using queries that combine visual and textual cues, is crucial for numerous real-world applicat…
Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering
Yanbin Wei, Jiangyue Yan, Chun Kang +4
Large Multimodal Models (LMMs) have shown generalized zero-shot capabilities in diverse domain question-answering (QA) tasks, including graph QA that involves complex graph topolog…
DynamicMind: A Tri-Mode Thinking System for Large Language Models
Wei Li, Yanbin Wei, Qiushi Huang +4
Modern large language models (LLMs) often struggle to dynamically adapt their reasoning depth to varying task complexities, leading to suboptimal performance or inefficient resourc…
CodeGraph: Enhancing Graph Reasoning of LLMs with Code
Qiaolong Cai, Zhaowei Wang, Shizhe Diao +2
With the increasing popularity of large language models (LLMs), reasoning on basic graph algorithm problems is an essential intermediate step in assessing their abilities to proces…
Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment
Zhili Liu, Yunhao Gou, Kai Chen +8
As the capabilities of large language models (LLMs) continue to expand, aligning these models with human values remains a significant challenge. Recent studies show that reasoning…
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion
Yanbin Wei, Qiushi Huang, James T. Kwok +1
Knowledge Graph Completion (KGC) is crucial for addressing knowledge graph incompleteness and supporting downstream applications. Many models have been proposed for KGC. They can b…
GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning
Yanbin Wei, Shuai Fu, Weisen Jiang +5
Large Language Models (LLMs) are increasingly used for various tasks with graph structures. Though LLMs can process graph information in a textual format, they overlook the rich vi…
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Longhui Yu, Weisen Jiang, Han Shi +7
Large language models (LLMs) have pushed the limits of natural language understanding and exhibited excellent problem-solving ability. Despite the great success, most existing open…
Forward-Backward Reasoning in Large Language Models for Mathematical Verification
Weisen Jiang, Han Shi, Longhui Yu +4
Self-Consistency samples diverse reasoning chains with answers and chooses the final answer by majority voting. It is based on forward reasoning and cannot further improve performa…