4 citations · 6 across the 3 of their papers we have counts for
5 papers
Optimizing open-domain question answering with graph-based retrieval augmented generation
Joyce Cahoon, Prerna Singh, Nick Litombe +6
In this work, we benchmark various graph-based retrieval-augmented generation (RAG) systems across a broad spectrum of query types, including OLTP-style (fact-based) and OLAP-style…
Explaining Categorical Feature Interactions Using Graph Covariance and LLMs
Cencheng Shen, Darren Edge, Jonathan Larson +1
Modern datasets often consist of numerous samples with abundant features and associated timestamps. Analyzing such datasets to uncover underlying events typically requires complex…
Principal Graph Encoder Embedding and Principal Community Detection
Cencheng Shen, Yuexiao Dong, Carey E. Priebe +3
In this paper, we introduce the concept of principal communities and propose a principal graph encoder embedding method that concurrently detects these communities and achieves ver…
Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery
Cencheng Shen, Jonathan Larson, Ha Trinh +1
This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community re…
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
Darren Edge, Ha Trinh, Newman Cheng +7
The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over pri…