AI知识助手：一种为会话式AI代理自动构建知识库的方法
AI Knowledge Assist: An Automated Approach for the Creation of Knowledge Bases for Conversational AI Agents
AI总结：
本文提出AI知识助手系统，从历史客服对话中自动提取问答对构建知识库，微调轻量级LLM在20家公司上达到90%以上准确率，消除冷启动差距，实现RAG聊天机器人快速部署。
摘要：
随着大型语言模型（LLMs）的快速发展，利用检索增强生成（RAG）技术解决客户问题的会话式AI系统的应用日益增多。然而，缺乏公司专用的知识库是将会话式AI系统集成到联络中心的主要障碍。为此，我们提出了AI知识助手（AI Knowledge Assist），该系统从历史客户与代理的对话中提取问答（QA）对形式的知识，以自动构建知识库。在内部数据上对轻量级LLM进行微调，展现了最先进的性能，优于更大的闭源LLM。具体而言，对20家公司的实证评估表明，所提出的利用LLaMA-3.1-8B模型的AI知识助手系统通过实现回答信息寻求型问题超过90%的准确率，消除了联络中心的冷启动差距。这使得基于RAG的聊天机器人能够立即部署。
英文摘要：
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.
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。
