27 citations · 67 across the 23 of their papers we have counts for
9 papers · 1 filter
Reasoning by Exploration: A Unified Approach to Retrieval and Generation over Graphs
Haoyu Han, Kai Guo, Harry Shomer +5
Reasoning over structured graphs remains a fundamental challenge for Large Language Models (LLMs), particularly when scaling to large graphs. Existing approaches typically follow t…
Beyond Static Retrieval: Opportunities and Pitfalls of Iterative Retrieval in GraphRAG
Kai Guo, Xinnan Dai, Shenglai Zeng +4
Retrieval-augmented generation (RAG) is a powerful paradigm for improving large language models (LLMs) on knowledge-intensive question answering. Graph-based RAG (GraphRAG) leverag…
Enhancing LLM-Based Short Answer Grading with Retrieval-Augmented Generation
Yucheng Chu, Peng He, Hang Li +6
Short answer assessment is a vital component of science education, allowing evaluation of students' complex three-dimensional understanding. Large language models (LLMs) that posse…
Empowering GraphRAG with Knowledge Filtering and Integration
Kai Guo, Harry Shomer, Shenglai Zeng +3
In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph…
Unveiling Mode Connectivity in Graph Neural Networks
Bingheng Li, Zhikai Chen, Haoyu Han +3
A fundamental challenge in understanding graph neural networks (GNNs) lies in characterizing their optimization dynamics and loss landscape geometry, critical for improving interpr…
Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases
Yongjia Lei, Haoyu Han, Ryan A. Rossi +5
Text-rich Graph Knowledge Bases (TG-KBs) have become increasingly crucial for answering queries by providing textual and structural knowledge. However, current retrieval methods of…
RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
Haoyu Han, Li Ma, Yu Wang +9
Retrieval-Augmented Generation (RAG) improves large language models (LLMs) by retrieving relevant information from external sources and has been widely adopted for text-based tasks…
Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning
Haoyu Han, Yaochen Xie, Hui Liu +8
Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks; however, they still encounter challenges in reasoning tasks that require understandi…
Retrieval-Augmented Generation with Graphs (GraphRAG)
Haoyu Han, Yu Wang, Harry Shomer +15
Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from…