面向客服问答的基于知识图谱的检索增强生成
Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering
AI总结：
本文提出将知识图谱与检索增强生成相结合的客服问答方法，通过保留历史工单结构信息提升检索与回答质量，在LinkedIn部署后中位解决时间缩短28.6%。
摘要：
在客户服务技术支持中，快速准确地检索相关历史问题对于高效解决客户咨询至关重要。面向大语言模型（LLMs）的检索增强生成（RAG）中的传统检索方法将大量历史问题追踪工单语料视为纯文本，忽略了关键的工单内部结构和工单间关系，从而限制了性能。我们提出了一种新颖的客户服务问答方法，将RAG与知识图谱（KG）相结合。我们的方法从历史问题中构建知识图谱用于检索，保留工单内部结构和工单间关系。在问答阶段，我们的方法解析消费者查询，并从知识图谱中检索相关子图以生成答案。这种知识图谱的集成不仅通过保留客户服务结构信息提高了检索准确性，还通过减轻文本切分的影响提升了回答质量。在我们基准数据集上的实证评估，采用关键检索指标（MRR、Recall@K、NDCG@K）和文本生成指标（BLEU、ROUGE、METEOR），表明我们的方法在MRR上比基线高出77.6%，在BLEU上高出0.32。我们的方法已在LinkedIn客户服务团队中部署约六个月，并将每个问题的中位解决时间缩短了28.6%。
英文摘要：
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 inter-issue 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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
