使用检索增强生成和知识图谱个性化大型语言模型
Personalizing Large Language Models using Retrieval Augmented Generation and Knowledge Graph
AI总结：
本文提出结合知识图谱与 RAG 的 LLM 个性化方法，利用结构化且持续更新的日历数据减少幻觉，实验表明其准确性优于文本输入基线且延迟适度降低。
摘要：
大型语言模型（LLM）的出现使众多应用成为可能，包括在聊天机器人和其他对话助手中利用其生成查询响应。尽管 LLM 在大量数据上进行训练，但它们往往出现严重过拟合，导致生成额外且不正确的数据，从而在输出生成中引发幻觉。此类问题的根本原因之一是提供给 LLM 的信息缺乏及时性、事实性和个性化。本文提出一种解决这些问题的方法：引入使用知识图谱（KG）的检索增强生成（RAG），以协助 LLM 生成面向用户定制的个性化响应。KG 的优势在于能够以结构化方式存储持续更新的事实信息。虽然我们的 KG 可用于各类频繁更新的个人数据，例如日历、联系人和位置数据，但本文重点关注日历数据。实验结果表明，与将个人数据作为文本输入的基线 LLM 相比，我们的方法在理解个人信息和生成准确响应方面表现显著更好，同时响应时间适度减少。
英文摘要：
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.
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。
