Apollonion：以用户画像为中心的对话智能体
Apollonion: Profile-centric Dialog Agent
AI总结：
针对当前大语言模型对话智能体缺乏用户个性化感知的问题，本研究提出Apollonion框架，通过构建并动态更新结构化用户画像生成个性化回复，同时配套了个性化效果评估方案。
摘要：
大语言模型（LLMs）的出现革新了对话智能体的发展。具体而言，一个训练良好的LLM作为核心处理单元，能够为用户的请求提供流畅且合理的回复。此外，研究人员还开发了各类辅助工具，如外部知识检索、用于生成生动回复的个性化角色、用于超长上下文管理的短期/长期记忆，完善了基于LLM的对话智能体的使用体验。然而，上述技术并未解决**从用户视角出发的个性化**问题：智能体对不同用户的回复方式千篇一律，没有考虑用户的习惯、兴趣、过往经历等特征。换言之，当前的对话智能体实现方案无法做到“了解用户”，对用户的精准描述与表征能力仍有待发展。在本研究中，我们提出了一套融合用户画像（含初始化、更新机制）的对话智能体框架：系统会分析用户的查询与回复，将其整理为结构化的用户画像，后续利用该画像生成更具个性化、更精准的回复。此外，我们还提出了一系列个性化评估方案，用于衡量回复对不同用户的专属匹配程度。\n该框架被命名为Apollonion，灵感源自古希腊阿波罗神庙（也被称为Apollonion）上“认识你自己”的铭文。目前将个性化融入LLM的研究尚少，Apollonion是在对话智能体应用中引导LLM生成符合个性化需求回复的开创性工作，同时配套了一套用于衡量个性化程度的评估方法。
英文摘要：
The emergence of Large Language Models (LLMs) has innovated the development of dialog agents. Specially, a well-trained LLM, as a central process unit, is capable of providing fluent and reasonable response for user's request. Besides, auxiliary tools such as external knowledge retrieval, personalized character for vivid response, short/long-term memory for ultra long context management are developed, completing the usage experience for LLM-based dialog agents. However, the above-mentioned techniques does not solve the issue of \textbf{personalization from user perspective}: agents response in a same fashion to different users, without consideration of their features, such as habits, interests and past experience. In another words, current implementation of dialog agents fail in ``knowing the user''. The capacity of well-description and representation of user is under development. In this work, we proposed a framework for dialog agent to incorporate user profiling (initialization, update): user's query and response is analyzed and organized into a structural user profile, which is latter served to provide personal and more precise response. Besides, we proposed a series of evaluation protocols for personalization: to what extend the response is personal to the different users. The framework is named as \method{}, inspired by inscription of ``Know Yourself'' in the temple of Apollo (also known as \method{}) in Ancient Greek. Few works have been conducted on incorporating personalization into LLM, \method{} is a pioneer work on guiding LLM's response to meet individuation via the application of dialog agents, with a set of evaluation methods for measurement in personalization.
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。
