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Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering
April 30, 2024, 4:43 a.m. | Zhentao Xu, Mark Jerome Cruz, Matthew Guevara, Tie Wang, Manasi Deshpande, Xiaofeng Wang, Zheng Li
cs.LG updates on arXiv.org arxiv.org
Abstract: In customer service technical support, swiftly and accurately retrieving relevant past issues is critical for efficiently resolving customer inquiries. The conventional retrieval methods in retrieval-augmented generation (RAG) for large language models (LLMs) treat a large corpus of past issue tracking tickets as plain text, ignoring the crucial intra-issue structure and inter-issue relations, which limits performance. We introduce a novel customer service question-answering method that amalgamates RAG with a knowledge graph (KG). Our method constructs a …
abstract arxiv cs.ai cs.ir cs.lg customer customer service graphs issue knowledge knowledge graphs language language models large language large language models llms question question answering rag retrieval retrieval-augmented service support technical text tickets tracking type
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