Apollonion: Profile-centric Dialog Agent
<|reference_start|>Apollonion: Profile-centric Dialog Agent: 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.<|reference_end|>
@article{chen2024apollonion:,
    title={Apollonion: Profile-centric Dialog Agent},
    author={Shangyu Chen, Zibo Zhao, Yuanyuan Zhao, Xiang Li},
    journal={arXiv preprint arXiv:2404.08692},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08692},
    primaryClass={cs.IR cs.AI cs.CL}
}