The authors introduce a novel customer service question-answering method that combines retrieval-augmented generation (RAG) with a knowledge graph (KG). The key insights are:
Intra-issue Structure Preservation: The method constructs a KG from historical customer service issue tickets, retaining the inherent structure and relationships between different sections of each ticket. This preserves crucial information that is lost when treating tickets as plain text.
Inter-issue Relation Modeling: The KG also captures the explicit and implicit connections between different issue tickets, enabling more accurate retrieval of relevant past issues.
Coherent Answer Generation: By leveraging the structured KG, the method can generate more complete and coherent answers by avoiding the issue of text segmentation, which can lead to the disconnection of related content.
The empirical evaluation shows that the proposed method outperforms the baseline by 77.6% in Mean Reciprocal Rank (MRR) for retrieval and 0.32 in BLEU score for answer generation. The method has also been deployed in LinkedIn's customer service team, reducing the median per-issue resolution time by 28.6%.
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by Zhentao Xu,M... at arxiv.org 04-30-2024
https://arxiv.org/pdf/2404.17723.pdfDeeper Inquiries