Apollonion: Profile-centric Dialog Agent
Chen Shangyu, Zhao Zibo, Zhao Yuanyuan, Li Xiang. Arxiv 2024
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 ofKnow
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.
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