用RAG增强大模型，帮客服高效生成精准回复。
RAG based Question-Answering for Contextual Response Prediction System
- 结合检索与生成，利用历史对话和知识库生成回复
- 自动化与人工评估均显示优于现有BERT方法
- 适合需要高准确率的零售客服场景
大语言模型（LLMs）在自然语言处理任务中展现出强大能力，尤其在问答系统方面潜力巨大。但在实际工业场景中，为确保对客户查询的回应准确且相关，需借助全面的知识库以避免幻觉问题。检索增强生成（RAG）成为应对该挑战的有力方案。然而，构建面向真实应用的高精度问答框架仍面临三大难题：数据可用性不足、生成内容质量难以评估，以及人工评价成本高昂。本文提出一个端到端框架，将具备RAG能力的LLMs应用于实际产业场景。针对客户提问，系统检索相关知识文档，并结合之前的聊天记录，为大型零售公司客服中心的客服人员生成响应建议。通过全面的自动化与人工评估，结果表明该方案在准确性和相关性上优于当前基于BERT的算法。研究证实，基于RAG的LLM可有效减轻人类客服的工作负担，成为理想辅助工具。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have shown versatility in various Natural Language Processing (NLP) tasks, including their potential as effective question-answering systems. However, to provide precise and relevant information in response to specific customer queries in industry settings, LLMs require access to a comprehensive knowledge base to avoid hallucinations. Retrieval Augmented Generation (RAG) emerges as a promising technique to address this challenge. Yet, developing an accurate question-answering framework for real-world applications using RAG entails several challenges: 1) data availability issues, 2) evaluating the quality of generated content, and 3) the costly nature of human evaluation. In this paper, we introduce an end-to-end framework that employs LLMs with RAG capabilities for industry use cases. Given a customer query, the proposed system retrieves relevant knowledge documents and leverages them, along with previous chat history, to generate response suggestions for customer service agents in the contact centers of a major retail company. Through comprehensive automated and human evaluations, we show that this solution outperforms the current BERT-based algorithms in accuracy and relevance. Our findings suggest that RAG-based LLMs can be an excellent support to human customer service representatives by lightening their workload.
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