用知识图谱增强大模型，让聊天机器人更懂用户个人情况。
Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph
- 用知识图谱结构化存储用户日程等信息，辅助大模型生成个性化回复。
- 实验显示该方法在理解个人数据上显著优于传统文本输入基线。
- 适合需要精准个性化服务的对话系统开发者使用。
大型语言模型（LLMs）在聊天机器人等对话应用中展现出广泛应用潜力。然而，由于训练数据庞杂，模型常出现过拟合，导致输出中产生额外或错误信息，引发幻觉问题。其根源之一是缺乏及时、准确且个性化的信息输入。本文提出一种基于知识图谱（KG）的检索增强生成（RAG）方法，利用持续更新的结构化事实信息（如日历、联系人、位置数据）提升大模型对用户个性化需求的理解与响应准确性。本文聚焦于日历数据的应用。实验结果表明，相较于将个人数据作为纯文本输入的基线方法，本方案在理解个人上下文和生成准确回复方面表现显著更优，同时仅带来适度的响应时间增加。
原文摘要 · Abstract (English)
The advent of large language models (LLMs) has allowed numerous applications, including the generation of queried responses, to be leveraged in chatbots and other conversational assistants. Being trained on a plethora of data, LLMs often undergo high levels of over-fitting, resulting in the generation of extra and incorrect data, thus causing hallucinations in output generation. One of the root causes of such problems is the lack of timely, factual, and personalized information fed to the LLM. In this paper, we propose an approach to address these problems by introducing retrieval augmented generation (RAG) using knowledge graphs (KGs) to assist the LLM in personalized response generation tailored to the users. KGs have the advantage of storing continuously updated factual information in a structured way. While our KGs can be used for a variety of frequently updated personal data, such as calendar, contact, and location data, we focus on calendar data in this paper. Our experimental results show that our approach works significantly better in understanding personal information and generating accurate responses compared to the baseline LLMs using personal data as text inputs, with a moderate reduction in response time.
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