7 citations · 7 across the 5 of their papers we have counts for
6 papers
ASTRA-QA: A Benchmark for Abstract Question Answering over Documents
Shu Wang, Shansong Zhou, Xinyang Wang +3
Document-based question answering (QA) increasingly includes abstract questions that require synthesizing scattered information from long documents or across multiple documents int…
SkillRAE: Agent Skill-Based Context Compilation for Retrieval-Augmented Execution
Xiangcheng Meng, Shu Wang, Yixiang Fang
Large Language Model (LLM)-based agents (e.g., OpenClaw) increasingly rely on reusable skill libraries to solve artifact-rich tasks such as document-centric workflows and data-inte…
LLM+Graph@VLDB'2025 Workshop Summary
Yixiang Fang, Arijit Khan, Tianxing Wu +2
The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing strong interest from both academia and…
BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents
Shu Wang, Yingli Zhou, Yixiang Fang
As an effective method to boost the performance of Large Language Models (LLMs) on the question answering (QA) task, Retrieval-Augmented Generation (RAG), which queries highly rele…
In-depth Analysis of Graph-based RAG in a Unified Framework
Yingli Zhou, Yaodong Su, Youran Sun +8
Graph-based Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs), improving their factual accuracy, adaptab…
ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation
Shu Wang, Yixiang Fang, Yingli Zhou +2
Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs) for solving question-answer (QA) tasks. The state-of-t…