Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering
Zhentao Xu, Missy Meine C. Dela Cruz, Matthew Guevara et al.
2024 · 125 citations
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 KG from historical issues for use in r…
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