Retrieval-Augmented Generation with Knowledge Graphs for Customer Support Q/A (Paper Summary)
#rag #knowledgegraph #customersupport #machinelearning #llms #naturallanguageprocessing
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 retrieval, retaining the intra-issue structure and interissue relations. During the question-answering phase, our method parses consumer queries and retrieves related sub-graphs from the KG to generate answers. This integration of a KG not only improves retrieval accuracy by preserving customer service structure information but also enhances answering quality by mitigating the effects of text segmentation. Empirical assessments on our benchmark datasets, utilizing key retrieval (MRR, Recall@K, NDCG@K) and text generation (BLEU, ROUGE, METEOR) metrics, reveal that our method outperforms the baseline by 77.6% in MRR and by 0.32 in BLEU. Our method has been deployed within LinkedIn’s customer service team for approximately six months and has reduced the median per-issue resolution time by 28.6%.
Paper Link: https://arxiv.org/pdf/2404.17723v1
Paper Title: Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering
Paper Author: Zhentao Xu, Mark Jerome Cruz, Matthew Guevara, Tie Wang, Manasi Deshpande, Xiaofeng Wang, Zheng Li
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I am Prakhar Mishra and this channel is my passion project. I am currently pursuing my MS (by research) in Data Science. I have an industry work-ex of 4+ years in the field of Data Science and Machine Learning with a particular focus on Natural Language Processing (NLP).