# Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data

arXiv: https://arxiv.org/abs/2406.18921 · HTML: https://mlsanity.com/paper/2406.18921/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Character-LLM: A Trainable Agent for Role-Playing](https://mlsanity.com/paper/2310.10158/) (2023)
- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024)
- [InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews](https://mlsanity.com/paper/2310.17976/) (2023)
- [RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models](https://mlsanity.com/paper/2310.00746/) (2023)
- [Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent](https://mlsanity.com/paper/2402.13717/) (2024)
- [Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works](https://mlsanity.com/paper/2404.12726/) (2024)

## Built on by (most cited first, 17 of 17)

- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025)
- [MINDECHO: Role-Playing Language Agents for Key Opinion Leaders](https://mlsanity.com/paper/2407.05305/) (2024)
- [STEER-BENCH: A Benchmark for Evaluating the Steerability of Large Language Models](https://mlsanity.com/paper/2505.20645/) (2025)
- [HyCoRA: Hyper-Contrastive Role-Adaptive Learning for Role-Playing](https://mlsanity.com/paper/2511.08017/) (2025)
- [AI Agent Behavioral Science](https://mlsanity.com/paper/2506.06366/) (2025)
- [Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents](https://mlsanity.com/paper/2503.08193/) (2025)
- [PsyMem: Fine-grained psychological alignment and Explicit Memory Control for Advanced Role-Playing LLMs](https://mlsanity.com/paper/2505.12814/) (2025)
- [Video2Roleplay: A Multimodal Dataset and Framework for Video-Guided Role-playing Agents](https://mlsanity.com/paper/2509.15233/) (2025)
- [Integrating Personality into Digital Humans: A Review of LLM-Driven Approaches for Virtual Reality](https://mlsanity.com/paper/2503.16457/) (2025)
- [Role-Playing Agents Driven by Large Language Models: Current Status, Challenges, and Future Trends](https://mlsanity.com/paper/2601.10122/) (2026)
- [Adaptive Turn-Taking for Real-time Multi-Party Voice Agents](https://mlsanity.com/paper/2606.13544/) (2026)
- [Do AI Personas Grow? Analyzing and Benchmarking Personality Evolution in LLM Agents After Life Events](https://mlsanity.com/paper/2608.06485/) (2026)
- [Score Before You Speak: Improving Persona Consistency in Dialogue Generation using Response Quality Scores](https://mlsanity.com/paper/2508.06886/) (2025)
- [IROTE: Human-like Traits Elicitation of Large Language Model via In-Context Self-Reflective Optimization](https://mlsanity.com/paper/2508.08719/) (2025)
- [Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events](https://mlsanity.com/paper/2505.16455/) (2025)
- [PersonaManifold: Revealing and Exploiting Curved Geometry in LLM Persona Representations](https://mlsanity.com/paper/2609.34571/) (2026)

## Abstract (arXiv)

Role-playing agents (RPA) have been a popular application area for large language models (LLMs), attracting significant interest from both industry and academia.While existing RPAs well portray the characters' knowledge and tones, they face challenges in capturing their minds, especially for small role-playing language models (RPLMs). In this paper, we propose to enhance RPLMs via personality-indicative data. Specifically, we leverage questions from psychological scales and distill advanced RPAs to generate dialogues that grasp the minds of characters. Experimental results validate that RPLMs trained with our dataset exhibit advanced role-playing capabilities for both general and personality-related evaluations. Code and data are available at hrefhttps://github.com/alienet1109/RolePersonalitythis URL.