自动从对话记录中提取问答对，快速构建企业专属知识库。
AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents
- 从历史客服对话中自动提取QA对构建知识库
- 使用轻量模型微调后准确率超90%
- 适合需要快速部署聊天机器人的企业
随着大语言模型（LLMs）的快速发展，利用检索增强生成（RAG）技术的对话式AI系统在解决客户问题方面日益普及。然而，缺乏企业专属的知识库仍是接触中心集成对话式AI的主要障碍。为此，我们提出AI Knowledge Assist系统，通过从历史客户-客服对话中提取问答对，自动构建知识库。在20家公司的实证评估中，该系统基于LLaMA-3.1-8B模型微调后，可使信息查询类问题回答准确率超过90%，有效消除接触中心的冷启动问题，实现RAG驱动聊天机器人的即刻部署。
原文摘要 · Abstract (English)
The utilization of conversational AI systems by leveraging Retrieval Augmented Generation (RAG) techniques to solve customer problems has been on the rise with the rapid progress of Large Language Models (LLMs). However, the absence of a company-specific dedicated knowledge base is a major barrier to the integration of conversational AI systems in contact centers. To this end, we introduce AI Knowledge Assist, a system that extracts knowledge in the form of question-answer (QA) pairs from historical customer-agent conversations to automatically build a knowledge base. Fine-tuning a lightweight LLM on internal data demonstrates state-of-the-art performance, outperforming larger closed-source LLMs. More specifically, empirical evaluation on 20 companies demonstrates that the proposed AI Knowledge Assist system that leverages the LLaMA-3.1-8B model eliminates the cold-start gap in contact centers by achieving above 90% accuracy in answering information-seeking questions. This enables immediate deployment of RAG-powered chatbots.
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