Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering

arXiv:2404.17723v1 Announce Type: cross
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