Summary of Hybgrag: Hybrid Retrieval-augmented Generation on Textual and Relational Knowledge Bases, by Meng-chieh Lee et al.
HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases
by Meng-Chieh Lee, Qi Zhu, Costas Mavromatis, Zhen Han, Soji Adeshina, Vassilis N. Ioannidis, Huzefa Rangwala, Christos Faloutsos
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of retrieving relevant information from a semi-structured knowledge base (SKB) to answer user questions. Existing methods like Retrieval-Augmented Generation (RAG) and Graph RAG (GRAG) struggle with hybrid questions that require both textual and relational information. The authors identify key insights highlighting the limitations of these approaches and propose HybGRAG, a novel method for hybrid question answering (HQA) over SKB. HybGRAG consists of a retriever bank and a critic module, offering advantages such as agentic refining, adaptivity to hybrid questions, interpretability through refinement paths, and effectiveness in surpassing baselines on HQA benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hybrid question answering is important for retrieving relevant information from semi-structured knowledge bases. The authors propose a new method called HybGRAG that helps large language models answer complex questions. This approach uses both textual and relational information to provide accurate answers. It’s an improvement over previous methods like RAG and GRAG, which struggled with hybrid questions. By refining its output and providing explanations for its decisions, HybGRAG can help users get the answers they need. |
Keywords
» Artificial intelligence » Knowledge base » Question answering » Rag » Retrieval augmented generation
