Papers by Zengyi Gao
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs (2025.findings-acl)
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| Challenge: | Existing methods for retrieval-augmented generation struggle with a trade-off between flexibility and retrieval quality. |
| Approach: | They propose a flexible modular KG-RAG framework that uses query text instead of KGs . they propose to use query text to infer the structural information of reasoning paths . |
| Outcome: | The proposed method achieves state-of-the-art performance with high efficiency and low resource consumption. |
See or Say Graphs: Agent-Driven Scalable Graph Understanding with Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing studies have explored textual graph descriptions and visual modalities for VLMs to understand graphs. |
| Approach: | They propose a unified framework that enhances both scalability and modality coordination in graph understanding by integrating textual and visual modalities. |
| Outcome: | GraphVista scales to large graphs, 200 larger than those used in existing benchmarks, and consistently outperforms existing textual, visual, and fusion-based methods. |
GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding (2025.acl-long)
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| Challenge: | Large language models struggle with comprehending graphical structure information through prompts of graph description sequences, especially as the graph size increases. |
| Approach: | They propose a framework to improve LLMs’ comprehension of both macro- and micro-level graphical information by placing critical graphical data in positions where LLM's exhibit stronger memory performance. |
| Outcome: | The proposed framework outperforms all other graph description methods in understanding graph structures of varying sizes. |