# Apollonion: Profile-centric Dialog Agent arXiv: https://arxiv.org/abs/2404.08692 ยท HTML: https://mlsanity.com/paper/2404.08692/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. - Builds on 41 of its 52 references that are in the map. - First posted on arXiv 10 Apr 2024. ## Placed in - LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/) ## Builds on (most cited first, 30 of 41) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023) - [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022) - [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023) - [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024) - [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019) - [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018) - [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020) - [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023) - [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023) - [How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://mlsanity.com/paper/1603.08023/) (2016) - [Character-LLM: A Trainable Agent for Role-Playing](https://mlsanity.com/paper/2310.10158/) (2023) - [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023) - [On the Planning Abilities of Large Language Models : A Critical Investigation](https://mlsanity.com/paper/2305.15771/) (2023) - [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023) - [Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions](https://mlsanity.com/paper/2306.02224/) (2023) - [RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models](https://mlsanity.com/paper/2310.00746/) (2023) - [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018) - [Training Millions of Personalized Dialogue Agents](https://mlsanity.com/paper/1809.01984/) (2018) - [Survey on Evaluation Methods for Dialogue Systems](https://mlsanity.com/paper/1905.04071/) (2019) - [Long Time No See! Open-Domain Conversation with Long-Term Persona Memory](https://mlsanity.com/paper/2203.05797/) (2022) - [Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment](https://mlsanity.com/paper/2401.12474/) (2024) - [Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses](https://mlsanity.com/paper/1708.07149/) (2017) - [On Generative Agents in Recommendation](https://mlsanity.com/paper/2310.10108/) (2023) - [A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data](https://mlsanity.com/paper/1911.04700/) (2019) - [Few-Shot Bot: Prompt-Based Learning for Dialogue Systems](https://mlsanity.com/paper/2110.08118/) (2021) - [Prompted LLMs as Chatbot Modules for Long Open-domain Conversation](https://mlsanity.com/paper/2305.04533/) (2023) ## Built on by (most cited first, 4 of 4) - [Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing](https://mlsanity.com/paper/2405.07726/) (2024) - [Persona2Web: Benchmarking Personalized Web Agents for Contextual Reasoning with User History](https://mlsanity.com/paper/2602.17003/) (2026) - [ProfiLLM: An LLM-Based Framework for Implicit Profiling of Chatbot Users](https://mlsanity.com/paper/2506.13980/) (2025) - [Matching Game Preferences Through Dialogical Large Language Models: A Perspective](https://mlsanity.com/paper/2507.20000/) (2025) ## Abstract (arXiv) 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 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.