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G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

A method for question answering on textual graphs using retrieval-augmented generation.

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G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

By Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, Bryan HooiarXiv (Cornell University)
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The authors propose G-Retriever, a framework for question-answering on textual graphs. They introduce a new approach called retrieval-augmented generation (RAG) and formulate the task as a Prize-Collecting Steiner Tree optimization problem to mitigate hallucination.

The method is evaluated on various textual graph tasks and outperforms baselines.

Abstract

The authors propose G-Retriever, a framework for question-answering on textual graphs. They introduce a new approach called retrieval-augmented generation (RAG) and formulate the task as a Prize-Collecting Steiner Tree optimization problem to mitigate hallucination. The method is evaluated on various textual graph tasks and outperforms baselines.

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textual graphsquestion answeringgraph neural networksconversational interfacegraph understandingsoft promptingKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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