Two Tales of Persona in LLMs:
A Survey of Role-Playing and Personalization

Overview

Introduction

This is the official repository of the paper "Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization".

The concept of persona, originally adopted in dialogue literature, has re-surged as a promising framework for tailoring large language models (LLMs) to specific context (e.g., personalized search, LLM-as-a-judge). However, the growing research on leveraging persona in LLMs is relatively disorganized and lacks a systematic taxonomy. To close the gap, we present a comprehensive survey to categorize the current state of the field. We identify two lines of research, namely (1) LLM Role-Playing, where personas are assigned to LLMs, and (2) LLM Personalization, where LLMs take care of user personas. Additionally, we introduce existing methods for LLM personality evaluation. To the best of our knowledge, we present the first survey for role-playing and personalization in LLMs under the unified view of persona.

We continuously maintain this paper collection to foster future endeavors.

## News - [2024.06.27] :fire: We update an 8-page version on [arXiv](https://arxiv.org/abs/2406.01171). - [2024.06.04] :rocket: Our paper is now available on [arXiv](https://arxiv.org/abs/2406.01171) and the reading list on [GitHub](https://github.com/MiuLab/PersonaLLM-Survey). ## Table of Contents - [🙆‍♀️ LLM Role-Play (Adapt to Environment)](#llm-role-play-adapt-to-environment) - [💼 Workshops](#role-playing-workshops) - [🌎 Environments](#environments) - [💻 Software Development](#software-development) - [🌐 Web](#web) - [🎮 Game](#game) - [🏥 Medical Application](#medical-application) - [🧑‍⚖️ LLM as Evaluators](#llm-as-evaluators) - [📦 General Framework](#general-framework) - [🤖 Agentic Interactions](#agentic-interactions) - [📊 Schemas](#schemas) - [👤 Single-Agent](#single-agent) - [👥 Multi-Agent](#multi-agent) - [💡 Emergent Behaviors](#emergent-behaviors) - [🙆‍♂️ LLM Personalization (Adapt to User)](#llm-personalization-adapt-to-user) - [💼 Workshops & Competitions](#personalization-workshops) - [📌 Tasks](#tasks) - [💬 Personalized Dialogue](#personalized-dialogue) - [🔧 ToD Modeling](#tod-modeling) - [📝 User Persona Modeling](#user-persona-modeling) - [🛒 Recommendation System](#recommendation-system) - [🔍 Personalized Search](#personalized-search) - [🩺 Personalized Healthcare](#personalized-healthcare) - [📚 Personalized Education](#personalized-education) - [🛠️ Methods](#methods) - [🎛️ Fine-Tuning](#fine-tuning) - [🔗 Retrieval Augmentation](#retrieval-augmentation) - [✍️ Prompting](#prompting) - [📄 Vanilla Personalized Prompt](#vanilla-personalized-prompt) - [🔦 Retrieval-Augmented Personalized Prompt](#retrieval-augmented-personalized-prompt) - [📂 Profile-Augmented Prompt](#profile-augmented-prompt) - [🧐 LLM Personality Evaluation](#llm-personality-evaluation) - [🌱 How to contribute](#how-to-contribute) - [🔖 Citation](#citation) - [🖌️ Authors](#authors)

🙆‍♀️ LLM Role-Play (Adapt to Environment)

LLMs are tasked to play the assigned personas (i.e., roles) and act accordance to environmental feedback. The key aspect is *how LLMs adapt to defined environments*.

💼 Workshops

| Date | Workshop | Website Link | |:-----:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2405 | LLMAgent @ ICLR | [ICLR 2024 Workshop on Large Language Model (LLM) Agents](https://llmagents.github.io/) | | 2405 | Agent Workshop @ CMU | [CMU Agent Workshop 2024](https://cmu-agent-workshop.github.io/) |

🌎 Environments

💻 Software Development

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2308 | Hong et al. | ICLR | [MetaGPT: Meta Programming for Multi-Agent Collaborative Framework](https://arxiv.org/abs/2308.00352) | | 2307 | Qian et al. | arXiv | [Communicative agents for software development](https://arxiv.org/abs/2307.07924) | | 2305 | Dong et al. | TOSEM | [Self-collaboration code generation via chatgpt](https://arxiv.org/abs/2304.07590) | | 2107 | Chen et al. | arXiv | [Evaluating large language models trained on code](https://arxiv.org/abs/2107.03374) |

🌐 Web

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2404 | Liu et al. | arXiv | [VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding?](https://arxiv.org/abs/2404.05955v1) | | 2401 | Zheng et al. | LLMAgent @ ICLR | [GPT-4V(ision) is a Generalist Web Agent, if Grounded](https://arxiv.org/abs/2401.01614) | | 2401 | Koh et al. | LLMAgent @ ICLR | [VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks](https://arxiv.org/abs/2401.13649) | | 2401 | Cheng et al. | LLMAgent @ ICLR | [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://arxiv.org/abs/2401.10935) | | 2312 | Gur et al. | EMNLP | [Understanding HTML with Large Language Models](https://aclanthology.org/2023.findings-emnlp.185/) | | 2312 | Hong et al. | arXiv | [CogAgent: A Visual Language Model for GUI Agents](https://arxiv.org/abs/2312.08914) | | 2307 | Gur et al. | ICLR | [A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis](https://arxiv.org/abs/2307.12856) | | 2307 | Zhou et al. | ICLR | [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://arxiv.org/abs/2307.13854) | | 2306 | Deng et al. | NeurIPS | [Mind2web: Towards a generalist agent for the web](https://arxiv.org/abs/2306.06070) | | 2303 | Kim et al. | NeurIPS | [Language Models can Solve Computer Tasks](https://arxiv.org/abs/2303.17491) |

🎮 Game

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2310 | Wang et al. | EMNLP | [Humanoid Agents: Platform for Simulating Human-like Generative Agents](https://arxiv.org/abs/2310.05418) | | 2305 | Wang et al. | TMLR | [Voyager: An open-ended embodied agent with large language models](https://arxiv.org/abs/2305.16291) | | 2305 | Fu et al. | arXiv | [Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback](https://arxiv.org/abs/2305.10142) | | 2304 | Park et al. | UIST | [Generative agents: Interactive simulacra of human behavior](https://arxiv.org/abs/2304.03442) |

🏥 Medical Application

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2312 | Kwon et al. | AAAI | [Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales](https://arxiv.org/abs/2312.07399) | | 2311 | Tang et al. | arXiv | [Medagents: Large language models as collaborators for zero-shot medical reasoning](https://arxiv.org/abs/2311.10537) | | 2307 | Wu et al. | ICLR | [Large Language Models Perform Diagnostic Reasoning](https://arxiv.org/abs/2307.08922) | | 2207 | Liévin et al. | arXiv | [Can large language models reason about medical questions?](https://arxiv.org/abs/2207.08143) |

🧑‍⚖️ LLM as Evaluators

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2308 | Chan et al. | ICLR | [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://arxiv.org/abs/2308.07201) | | 2303 | Wu et al. | NLPCC | [Large Language Models are Diverse Role-Players for Summarization Evaluation](https://arxiv.org/abs/2303.15078) |

📦 General Framework

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2405 | Ahn, et al | ACL Findings | [TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models](https://arxiv.org/abs/2405.18027) | | 2308 | Chen et al. | ICLR | [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://arxiv.org/abs/2308.10848) | | 2307 | Wang et al. | NAACL | [Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration](https://arxiv.org/abs/2307.05300) | | 2303 | Li et al. | NeurIPS | [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://arxiv.org/abs/2303.17760) |

🤖 Interaction & Behaviors

📊 Schemas

👤 Single-Agent

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2401 | Cheng et al. | LLMAgent @ ICLR | [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://arxiv.org/abs/2401.10935) | | 2401 | Zheng et al. | LLMAgent @ ICLR | [GPT-4V(ision) is a Generalist Web Agent, if Grounded](https://arxiv.org/abs/2401.01614) | | 2312 | Hong et al. | arXiv | [CogAgent: A Visual Language Model for GUI Agents](https://arxiv.org/abs/2312.08914) | | 2305 | Wang et al. | TMLR | [Voyager: An open-ended embodied agent with large language models](https://arxiv.org/abs/2305.16291) |

👥 Multi-Agent

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2311 | Tang et al. | arXiv | [Medagents: Large language models as collaborators for zero-shot medical reasoning](https://arxiv.org/abs/2311.10537) | | 2308 | Chen et al. | ICLR | [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://arxiv.org/abs/2308.10848) | | 2308 | Hong et al. | ICLR | [MetaGPT: Meta Programming for Multi-Agent Collaborative Framework](https://arxiv.org/abs/2308.00352) | | 2308 | Chan et al. | ICLR | [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://arxiv.org/abs/2308.07201) | | 2307 | Qian et al. | arXiv | [Communicative agents for software development](https://arxiv.org/abs/2307.07924) | | 2305 | Fu et al. | arXiv | [Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback](https://arxiv.org/abs/2305.10142) |

💡 Emergent Behaviors

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2311 | Tang et al. | arXiv | [Medagents: Large language models as collaborators for zero-shot medical reasoning](https://arxiv.org/abs/2311.10537) | | 2308 | Chen et al. | ICLR | [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://arxiv.org/abs/2308.10848) | | 2307 | Wang et al. | NAACL | [Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration](https://arxiv.org/abs/2307.05300) | | 2305 | Fu et al. | arXiv | [Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback](https://arxiv.org/abs/2305.10142) | | 2303 | Li et al. | NeurIPS | [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://arxiv.org/abs/2303.17760) |

🙆‍♂️ LLM Personalization (Adapt to User)

LLMs are tasked to take care of users’ personas (e.g., background information, or historical behaviors) to meet customized needs. The key aspect is *how LLMs adapt to distinct users*.

💼 Workshops & Competitions

📌 Tasks

💬 Personalized Dialogue

🔧 ToD Modeling

LLMs Era | Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2305 | Yang et al. | EMNLP | [RefGPT: Dialogue Generation of GPT, by GPT, and for GPT](https://aclanthology.org/2023.findings-emnlp.165/) | | 2302 | Li et al. | NeurIPS | [Guiding large language models via directional stimulus prompting](https://arxiv.org/abs/2302.11520) | | 2005 | Hosseini-Asl et al. | NeurIPS | [A Simple Language Model for Task-Oriented Dialogue](https://arxiv.org/abs/2005.00796) |

Comprehensive Paper List

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2312 | Xu et al. | EMNLP | [Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data](https://aclanthology.org/2023.emnlp-main.385/) | | 2309 | Hu et al. | arXiv | [Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals](https://arxiv.org/abs/2309.08949) | | 2308 | Wu et al. | SIGDIAL | [DiactTOD: Learning Generalizable Latent Dialogue Acts for Controllable Task-Oriented Dialogue Systems](https://aclanthology.org/2023.sigdial-1.24.pdf) | | 2305 | Yang et al. | EMNLP | [RefGPT: Dialogue Generation of GPT, by GPT, and for GPT](https://aclanthology.org/2023.findings-emnlp.165/) | | 2305 | Bang et al. | ACL | [Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System](https://aclanthology.org/2023.findings-acl.464/) | | 2304 | Ashby et al. | CHI | [Personalized Quest and Dialogue Generation in Role-Playing Games: A Knowledge Graph and Language Model-based Approach](https://dl.acm.org/doi/10.1145/3544548.3581441) | | 2304 | Hudevcek et al. | SIGDIAL | [Are Large Language Models All You Need for Task-Oriented Dialogue?](https://aclanthology.org/2023.sigdial-1.21/) | | 2302 | Li et al. | NeurIPS | [Guiding large language models via directional stimulus prompting](https://arxiv.org/abs/2302.11520) | | 2302 | Feng et al. | ICLR | [Fantastic rewards and how to tame them: A case study on reward learning for task-oriented dialogue systems](https://arxiv.org/abs/2302.10342) | | 2210 | Huryn et al. | COLING | [Automatic Generation of Large-scale Multi-turn Dialogues from Reddit](https://aclanthology.org/2022.coling-1.297/) | | 2108 | Peng et al. | TACL | [Soloist: Building Task Bots at Scale with Transfer Learning and Machine Teaching](https://aclanthology.org/2021.tacl-1.49/) | | 2012 | Yang et al. | AAAI | [UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2](https://arxiv.org/abs/2012.03539) | | 2008 | Madotto et al. | arXiv | [Language models as few-shot learner for task-oriented dialogue systems](https://arxiv.org/abs/2008.06239) | | 2005 | Hosseini-Asl et al. | NeurIPS | [A Simple Language Model for Task-Oriented Dialogue](https://arxiv.org/abs/2005.00796) |

--- Pre-LLMs Era | Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2006 | Jianhong Wang et al. | ICLR | [Modelling hierarchical structure between dialogue policy and natural language generator with option framework for task-oriented dialogue system](https://arxiv.org/abs/2006.06814) | | 1606 | N. Mrksic et al. | ACL | [Neural Belief Tracker: Data-Driven Dialogue State Tracking](https://aclanthology.org/P17-1163/) | | 1506 | Alessandro Sordoni et al. | NAACL | [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](https://aclanthology.org/N15-1020/) |

Comprehensive Paper List

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2105 | Sun et al. | SIGIR | [Simulating user satisfaction for the evaluation of task-oriented dialogue systems](https://arxiv.org/abs/2105.03748) | | 2006 | Wang et al. | ICLR | [Modelling hierarchical structure between dialogue policy and natural language generator with option framework for task-oriented dialogue system](https://arxiv.org/abs/2006.06814) | | 2003 | Yang et al. | IEEE | [Multitask Learning and Reinforcement Learning for Personalized Dialog Generation: An Empirical Study](https://ieeexplore.ieee.org/abstract/document/9025776) | | 1912 | Huang | AAAI | [MALA: Cross-Domain Dialogue Generation with Action Learning](https://arxiv.org/abs/1912.08442) | | 1910 | Zhang et al. | *SEM | [Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking](https://aclanthology.org/2020.starsem-1.17/) | | 1905 | Wu et al. | ACL | [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://aclanthology.org/P19-1078/) | | 1807 | Lei et al. | ACL | [Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures](https://aclanthology.org/P18-1133/) | | 1804 | Liu et al. | NAACL | [Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems](https://aclanthology.org/N18-1187/) | | 1712 | Rastogi et al. | IEEE | [Scalable Multi-Domain Dialogue State Tracking](https://arxiv.org/abs/1712.10224) | | 1709 | Wu et al. | AAAI | [StarSpace: Embed all the things!](https://arxiv.org/abs/1709.03856) | | 1606 | Miller et al. | EMNLP | [Key-Value Memory Networks for Directly Reading Documents](https://aclanthology.org/D16-1147/) | | 1606 | Mrksic et al. | ACL | [Neural Belief Tracker: Data-Driven Dialogue State Tracking](https://aclanthology.org/P17-1163/) | | 1506 | Sordoni et al. | NAACL | [A Neural Network Approach to Context-Sensitive Generation of Conversational Responses](https://aclanthology.org/N15-1020/) |

📝 User Persona Modeling

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2405 | Han | ACL | [PSYDIAL: Personality-based Synthetic Dialogue Generation Using Large Language Models](https://aclanthology.org/2024.lrec-main.1166/) | | 2307 | Tang et al. | ACL | [Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona](https://aclanthology.org/2023.acl-long.299/) | | 1807 | Zhang et al. | ACL | [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://aclanthology.org/P18-1205/) |

Comprehensive Paper List

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2405 | Han | ACL | [PSYDIAL: Personality-based Synthetic Dialogue Generation Using Large Language Models](https://aclanthology.org/2024.lrec-main.1166/) | | 2401 | Lotfi et al. | IEEE | [PersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traits](https://arxiv.org/abs/2401.07363) | | 2401 | Kim et al. | EACL | [Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement](https://arxiv.org/abs/2401.14215) | | 2308 | Tu et al. | arXiv | [CharacterChat: Learning towards Conversational AI with Personalized Social Support](https://arxiv.org/abs/2308.10278) | | 2307 | Tang et al. | ACL | [Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona](https://aclanthology.org/2023.acl-long.299/) | | 2307 | Ahn, et al | ACL | [MPCHAT: Towards Multimodal Persona-Grounded Conversation](https://aclanthology.org/2023.acl-long.189/) | | 2011 | Zhong et al. | EMNLP | [Towards Persona-Based Empathetic Conversational Models](https://aclanthology.org/2020.emnlp-main.531/) | | 2007 | Wu et al. | ACL | [Guiding Variational Response Generator to Exploit Persona](https://aclanthology.org/2020.acl-main.7/) | | 2007 | Liu et al. | ACL | [You Impress Me: Dialogue Generation via Mutual Persona Perception](https://aclanthology.org/2020.acl-main.131/) | | 1911 | Zheng et al. | AAAI | [A Pre-Training Based Personalized Dialogue Generation Model with Persona-Sparse Data](https://arxiv.org/abs/1911.04700) | | 1911 | Song et al. | AAAI | [Generating Persona Consistent Dialogues by Exploiting Natural Language Inference](https://arxiv.org/abs/1911.05889) | | 1807 | Zhang et al. | ACL | [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://aclanthology.org/P18-1205/) |

🛒 Recommendation System

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2305 | Yang et al. | arXiv | [PALR: Personalization Aware LLMs for Recommendation](https://arxiv.org/abs/2305.07622) | | 2304 | Wang et al. | arXiv | [Zero-Shot Next-Item Recommendation using Large Pretrained Language Models](https://arxiv.org/abs/2304.03153) | | 2108 | Li et al. | ACL | [Personalized Transformer for Explainable Recommendation](https://aclanthology.org/2021.acl-long.383/) |

Comprehensive Paper List

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2405 | Hu et al. | WWW | [Enhancing sequential recommendation via llm-based semantic embedding learning](https://dl.acm.org/doi/abs/10.1145/3589335.3648307) | | 2311 | Chen et al. | arXiv | [A Survey on Large Language Models for Personalized and Explainable Recommendations](https://arxiv.org/abs/2311.12338) | | 2308 | Wang et al. | arXiv | [RecMind: Large Language Model Powered Agent For Recommendation](https://arxiv.org/abs/2308.14296) | | 2308 | Chu et al. | arXiv | [Leveraging Large Language Models for Pre-trained Recommender Systems](https://arxiv.org/abs/2308.10837) | | 2306 | Li et al. | arXiv | [Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations](https://arxiv.org/abs/2306.01475) | | 2305 | Yang et al. | arXiv | [PALR: Personalization Aware LLMs for Recommendation](https://arxiv.org/abs/2305.07622) | | 2305 | Zhang et al. | arXiv | [Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach](https://arxiv.org/abs/2305.07001) | | 2304 | Wang et al. | arXiv | [Zero-Shot Next-Item Recommendation using Large Pretrained Language Models](https://arxiv.org/abs/2304.03153) | | 2208 | Chen et al. | KDD | [Personalized Chit-Chat Generation for Recommendation Using External Chat Corpora](https://dl.acm.org/doi/abs/10.1145/3534678.3539215) | | 2202 | Li et al. | TOIS | [Personalized Prompt Learning for Explainable Recommendation](https://arxiv.org/abs/2202.07371) | | 2108 | Li et al. | ACL | [Personalized Transformer for Explainable Recommendation](https://aclanthology.org/2021.acl-long.383/) |

🔍 Personalized Search

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2405 | Zhou et al. | WWW | [Cognitive personalized search integrating large language models with an efficient memory mechanism](https://arxiv.org/abs/2402.10548) | | 2405 | Baek et al. | WWW | [Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion](https://arxiv.org/abs/2311.06318) | | 2405 | Salemi | arXiv | [Unified ranking for multiple retrieval-augmented large language models](https://arxiv.org/abs/2405.00175) | | 2402 | Sharma et al. | CHI | [Generative echo chamber? effects of llm-powered search systems on diverse information seeking](https://arxiv.org/abs/2402.05880) | | 2307 | Eleni et al. | arXiv | [Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment](https://arxiv.org/abs/2307.03744) | | 2307 | Ziems et al. | ACL | [Large Language Models are Built-in Autoregressive Search Engines](https://arxiv.org/abs/2305.09612) | | 2107 | Zhou et al. | SIGIR | [Group based Personalized Search by Integrating Search Behaviour and Friend Network](https://arxiv.org/abs/2111.12618) |

🩺 Personalized Healthcare

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2402 | Abbasian et al. | arXiv | [Knowledge-Infused LLM-Powered Conversational Health Agent: A Case Study for Diabetes Patients](https://arxiv.org/abs/2402.10153) | | 2402 | Jin et al. | arXiv | [Health-LLM: Personalized Retrieval-Augmented Disease Prediction System](https://arxiv.org/abs/2402.00746) | | 2310 | Abbasian et al. | arXiv | [Conversational Health Agents: A Personalized LLM-Powered Agent Framework](https://arxiv.org/abs/2310.02374) | | 2309 | Zhang et al. | arXiv | [LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination](https://arxiv.org/abs/2309.11696) |

📚 Personalized Education

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2403 | Park et al. | CHI | [Empowering personalized learning through a conversation-based tutoring system with student modeling](https://arxiv.org/abs/2403.14071) | | 2308 | Dan et al. | arXiv | [Educhat: A large-scale language model-based chatbot system for intelligent education](https://arxiv.org/abs/2308.02773) | | 2307 | Shehata et al. | BEA @ ACL | [Enhancing Video-based Learning Using Knowledge Tracing: Personalizing Students’ Learning Experience with ORBITS](https://aclanthology.org/2023.bea-1.8/) |

🛠️ Methods

🎛️ Fine-Tuning

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2403 | Mondal et al. | EACL | [Presentations by the Humans and For the Humans: Harnessing LLMs for Generating Persona-Aware Slides from Documents](https://aclanthology.org/2024.eacl-long.163/) | | 2402 | Li et al. | arXiv | [Personalized Language Modeling from Personalized Human Feedback](https://arxiv.org/abs/2402.05133) | | 2402 | Tan et al. | arXiv | [Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning](https://arxiv.org/abs/2402.04401) | | 2312 | Hwang et al. | arXiv | [Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences](https://arxiv.org/abs/2312.09337) | | 2312 | Shea et al. | EMNLP | [Building Persona Consistent Dialogue Agents with Offline Reinforcement Learning](https://arxiv.org/abs/2310.10735) | | 2311 | Qin et al. | arXiv | [Enabling on-device large language model personalization with self-supervised data selection and synthesis](https://arxiv.org/abs/2311.12275) | | 2310 | Jang et al. | arXiv | [Personalized large language model alignment via post-hoc parameter merging](https://arxiv.org/abs/2310.11564) | | 2303 | Kirk et al. | arXiv | [Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback](https://arxiv.org/abs/2303.05453) |

🔗 Retrieval Augmentation

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2404 | Zhang et al. | arXiv | [Personalized LLM Response Generation with Parameterized Memory Injection](https://arxiv.org/abs/2404.03565) | | 2403 | Zhong et al. | AAAI | [MemoryBank: Enhancing Large Language Models with Long-Term Memory](https://ojs.aaai.org/index.php/AAAI/article/view/29946) | | 2402 | Sun et al. | arXiv | [Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement](https://arxiv.org/abs/2402.11060) | | 2402 | Tan et al. | arXiv | [Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning](https://arxiv.org/abs/2402.04401) | | 2205 | Fu et al. | ACL | [There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory](https://aclanthology.org/2022.acl-long.270/) | | 2106 | Wu et al. | NAACL | [Personalized Response Generation via Generative Split Memory Network](https://aclanthology.org/2021.naacl-main.157/) |

✍️ Prompting

📄 Vanilla Personalized Prompt

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2305 | Dai et al. | RecSys | [Uncovering ChatGPT’s Capabilities in Recommender Systems](https://arxiv.org/abs/2305.02182) | | 2305 | Christakopoulou et al. | arXiv | [Large Language Models for User Interest Journeys](https://arxiv.org/abs/2305.15498) | | 2305 | Zhiyuli et al. | arXiv | [BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model](https://arxiv.org/abs/2305.15673) |

🔦 Retrieval-Augmented Personalized Prompt

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2311 | Mysore et al. | arXiv | [PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers](https://arxiv.org/abs/2311.09180) | | 2308 | Li et al. | arXiv | [Teach LLMs to Personalize -- An Approach inspired by Writing Education](https://arxiv.org/abs/2308.07968) | | 2304 | Salemi et al. | arXiv | [LaMP: When Large Language Models Meet Personalization](https://arxiv.org/abs/2304.11406) |

📂 Profile-Augmented Prompt

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2405 | Li et al. | WWW | [Learning to Rewrite Prompts for Personalized Text Generation](https://dl.acm.org/doi/10.1145/3589334.3645408) | | 2310 | Richardson et al. | arXiv | [Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models](https://arxiv.org/abs/2310.20081) | | 2305 | Liu et al. | WSDM | [ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models](https://arxiv.org/abs/2305.06566) |

🧐 LLM Personality Evaluation

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2401 | Huang et al. | ICLR | [On the Humanity of Conversational AI: Evaluating the Psychological Portrayal of LLMs](https://openreview.net/forum?id=H3UayAQWoE) | | 2309 | Jiang et al. | NeurIPS | [Evaluating and inducing personality in pre-trained language models](https://arxiv.org/abs/2206.07550) | | 2307 | Fang et al. | ACL | [On Text-based Personality Computing: Challenges and Future Directions](https://aclanthology.org/2023.findings-acl.691/) |

Comprehensive Paper List

| Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 2403 | Sorokovikova et al. | PERSONALIZE @ EACL | [LLMs Simulate Big5 Personality Traits: Further Evidence](https://aclanthology.org/2024.personalize-1.7/) | | 2403 | Frisch et al. | PERSONALIZE @ EACL | [LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models](https://aclanthology.org/2024.personalize-1.9/) | | 2402 | Song et al. | arXiv | [Identifying Multiple Personalities in Large Language Models with External Evaluation](https://arxiv.org/abs/2402.14805) | | 2402 | Song et al. | arXiv | [Identifying Multiple Personalities in Large Language Models with External Evaluation](https://arxiv.org/abs/2402.14805) | | 2402 | Yang et al. | arXiv | [LLM Agents for Psychology: A Study on Gamified Assessments](https://arxiv.org/abs/2402.12326) | | 2401 | Huang et al. | ICLR | [On the Humanity of Conversational AI: Evaluating the Psychological Portrayal of LLMs](https://openreview.net/forum?id=H3UayAQWoE) | | 2312 | Rao et al. | EMNLP | [Can ChatGPT Assess Human Personalities? A General Evaluation Framework](https://aclanthology.org/2023.findings-emnlp.84/) | | 2311 | Dorner et al. | SoLaR @ NeurIPS | [Do personality tests generalize to large language models?](https://arxiv.org/abs/2311.05297) | | 2310 | Wang et al. | arXiv | [InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews](https://arxiv.org/abs/2310.17976) | | 2309 | Jiang et al. | NeurIPS | [Evaluating and inducing personality in pre-trained language models](https://arxiv.org/abs/2206.07550) | | 2307 | Pan et al. | arXiv | [Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models](https://arxiv.org/abs/2307.16180) | | 2307 | Fang et al. | ACL | [On Text-based Personality Computing: Challenges and Future Directions](https://aclanthology.org/2023.findings-acl.691/) | | 2307 | Ji et al. | arXiv | [Is ChatGPT a Good Personality Recognizer? A Preliminary Study](https://arxiv.org/abs/2307.03952) | | 2305 | Jiang et al. | arXiv | [Personallm: Investigating the ability of large language models to express big five personality traits](https://arxiv.org/abs/2305.02547) |

🌱 How to contribute

:sparkles: Welcome to contribute to this reading list via :memo: [Issues](https://github.com/MiuLab/PersonaLLM-Survey/issues) using the following format. | Date | Authors | Venue | Paper | |:-----:|:---------------:|:-----------:|:----------------------------------------------------------------------------------------------------------------------------| | 1706 | Vaswani, et al | NeurIPS | [Attention Is All You Need](https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) |

🔖 Citation

📚 If you find our survey beneficial for your research, please kindly cite our paper :-)

@misc{tseng2024talespersonallmssurvey,
  title={Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization},
  author={Yu-Min Tseng and Yu-Chao Huang and Teng-Yun Hsiao and Wei-Lin Chen and Chao-Wei Huang and Yu Meng and Yun-Nung Chen},
  year={2024},
  eprint={2406.01171},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2406.01171},
}

🖌️ Authors

Yu-Min Tseng*, Yu-Chao Huang*, [Teng-Yun Hsiao*](), Wei-Lin Chen*, Chao-Wei Huang, Yu Meng, Yun-Nung Chen.

(* Equal Contribution.) (Acknowlegement: Yu-Ching Hsu, Jia-Yin Foo.)

MiuLab / PersonaLLM-Survey

readme

Two Tales of Persona in LLMs:
A Survey of Role-Playing and Personalization

Introduction

🙆‍♀️ LLM Role-Play (Adapt to Environment)

💼 Workshops

🌎 Environments

💻 Software Development

🌐 Web

🎮 Game

🏥 Medical Application

🧑‍⚖️ LLM as Evaluators

📦 General Framework

🤖 Interaction & Behaviors

📊 Schemas

👤 Single-Agent

👥 Multi-Agent

💡 Emergent Behaviors

🙆‍♂️ LLM Personalization (Adapt to User)

💼 Workshops & Competitions

📌 Tasks

💬 Personalized Dialogue

🔧 ToD Modeling

📝 User Persona Modeling

🛒 Recommendation System

🔍 Personalized Search

🩺 Personalized Healthcare

📚 Personalized Education

🛠️ Methods

🎛️ Fine-Tuning

🔗 Retrieval Augmentation

✍️ Prompting

📄 Vanilla Personalized Prompt

🔦 Retrieval-Augmented Personalized Prompt

📂 Profile-Augmented Prompt

🧐 LLM Personality Evaluation

🌱 How to contribute

🔖 Citation

🖌️ Authors