Challenge: Existing dialogue datasets have a bias between query distributions and real-world user language usage.
Approach: They propose a framework for Chinese role-playing and a robust evaluation method . they propose specialized Chinese dialogue extraction model and specialized memory retrieval module .
Outcome: The proposed framework extracts character dialogue from novels and ensures high data quality.

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CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation (2024.acl-long)

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Challenge: CharacterEval is a benchmark for comprehensive RPCA assessment in Chinese . authors show that Chinese LLMs exhibit more promising capabilities than GPT-4 in role-playing conversation.
Approach: They propose a Chinese benchmark for comprehensive RPCA assessment . they use a dataset of Chinese role-playing dialogues and character profiles .
Outcome: The proposed benchmark demonstrates that Chinese LLMs exhibit more promising capabilities than GPT-4 in Chinese role-playing conversation.
Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data (2024.findings-emnlp)

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Challenge: Existing role-playing models focus on character knowledge and tones, but lack personality-indicative data to capture characters' minds.
Approach: They propose to enhance role-playing agents (RPAs) via personality-indicative data by asking psychological scales to capture broad aspects of personality traits in individuals.
Outcome: The proposed model exhibits advanced role-playing capabilities for both general and personality-related evaluations.
Large Language Models Meet Harry Potter: A Dataset for Aligning Dialogue Agents with Characters (2023.findings-emnlp)

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Challenge: Existing models that can create open-domain dialogue agents lack character representation and annotations.
Approach: They propose a dataset to study character alignment and character representation . it includes all dialogue sessions from the Harry Potter series and includes annotations .
Outcome: The proposed dataset can be used as a universal benchmark for character-driven LLMs.
Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works (2024.emnlp-main)

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Challenge: Recent advances in large language models (LLMs) have catalyzed numerous AI applications, among which role-playing agents (RPAs) are particularly popular.
Approach: They propose to evaluate LLMs' character understanding capability via the character profiling task, i.e., summarizing character profiles from corresponding materials, a widely adopted yet understudied practice for RPA development.
Outcome: The proposed model outperforms existing models and literature summarization methods and proves its ability to understand fictional characters in downstream tasks.
CharacterGPT: A Persona Reconstruction Framework for Role-Playing Agents (2025.naacl-industry)

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Challenge: Maintaining consistent character personas remains a significant challenge due to variability in information extraction.
Approach: They propose a framework to dynamically reconstruct character personas through Character Persona Training.
Outcome: The proposed framework is evaluated through Big Five personality evaluations and creative tasks, in which characters generate original narratives.
EmoCharacter: Evaluating the Emotional Fidelity of Role-Playing Agents in Dialogues (2025.naacl-long)

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Challenge: EmoCharacter evaluates emotional fidelity of role-playing agents in dialogues . current evaluations focus on personality fidelity, tone imitation, and knowledge consistency .
Approach: They propose a benchmark to assess emotional fidelity of role-playing agents in dialogues using large language models.
Outcome: The proposed benchmark measures emotional fidelity of role-playing agents and the characters they portray.
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)

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Challenge: Existing methods focus on knowledge and linguistic patterns of characters.
Approach: They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits.
Outcome: The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits.
Crafting Customisable Characters with LLMs: A Persona-Driven Role-Playing Agent Framework (2025.findings-emnlp)

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Challenge: Large Language Models (LLMs) are capable of generating human-like text, but the potential for freely customisable characters remains underexplored.
Approach: They propose a framework which employs Large Language Models to create freely customisable characters through personalised characteristic feature injection.
Outcome: The proposed framework provides valuable insights for developing more accurate and customisable human simulacra.
CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds (2025.naacl-long)

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Challenge: Evaluating role-playing capabilities in large language models is challenging due to complex dynamics involved in role-playering.
Approach: They propose a simulation sandbox that generates situational fine-grained character behavior trajectories to enhance LLM performance.
Outcome: The proposed model generates situational fine-grained character behavior trajectories to enhance performance.
Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents (2025.findings-acl)

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Challenge: Role-Playing Agents (RPAs) are increasingly popular due to diverse task requirements and agent designs.
Approach: They propose an evidence-based evaluation design guideline for LLM-based RPAs based on agent attributes, task attributes, and evaluation metrics.
Outcome: The proposed evaluation design guideline is based on a systematic review of 1,676 papers published between Jan. 2021 and Dec. 2024.

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