Challenge: Large Language Models are increasingly utilized as role-playing agents to simulate personas in interactive settings.
Approach: They propose a role-playing agent trained to explicitly ground responses in individual identity.
Outcome: The proposed agent can generate persona-consistent responses in long-context dialogues while maintaining general instruction-following capabilities.

Similar Papers

PersonaGym: Evaluating Persona Agents and LLMs (2025.findings-emnlp)

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Challenge: Persona agents are LLM agents conditioned to act according to an assigned persona . evaluating how faithfully these agents adhere to their personas remains a challenge .
Approach: a new study evaluates persona agents' ability to act according to an assigned persona . a persona agent's person score is a human-aligned automatic metric that can be used to evaluate a model .
Outcome: a new evaluation framework and a human-aligned automatic metric show that persona agents can perform better.
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.
Character is Destiny: Can Persona-assigned Language Models Make Personal Choices? (2025.findings-emnlp)

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Challenge: Recent research has demonstrated the potential of using LLMs to develop role-playing language agents (RPLAs) however, imitative decision-making necessitates a more nuanced understanding of personas.
Approach: They propose a method that uses persona-based memory retrieval to improve RPLAs.
Outcome: The proposed method significantly advances RPLAs on this task.
DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling (2025.acl-long)

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Challenge: Existing methods for generating personas from static historical data fail to capture dynamic behaviors and evolving preferences in real-world interactive scenarios.
Approach: They propose a novel approach that iteratively updates personas using streaming user behavior data to continually enhance their quality.
Outcome: The proposed approach delivers 32.2% reduction in user behavior prediction error over four update rounds, outperforming the best baseline by 22.92%.
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.
Investigating the Personality Consistency in Quantized Role-Playing Dialogue Agents (2024.emnlp-industry)

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Challenge: Using the Big Five personality traits model, we evaluate how stable assigned personalities are for Quantized Role-Playing Dialog Agents (QRPDA) during multi-turn interactions.
Approach: They propose a non-parametric method to evaluate the stability of assigned personalities in quantized large language models (LLMs) for role-playing scenarios.
Outcome: The proposed method shows that it maintains consistent personality traits in QRPDA, and it is more reliable in real-world applications.
Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness (2020.emnlp-main)

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Challenge: Existing models for improving consistency often train with additional NLI labels or attach trained extra modules to the generative agent.
Approach: They propose to encode personas into dialogue embeddings and a persona-conditioned dialogue dataset to improve persona consistency.
Outcome: The proposed approach can enforce dialogue agents to refrain from contradictions and improve consistency of existing models.
PersonaArena: Dynamic Simulation for Evaluating and Enhancing Persona-Level Role-Playing in Large Language Models (2026.findings-acl)

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Challenge: Existing research focuses on character-level settings and static evaluation formats fail to capture the complexity of everyday social interactions.
Approach: They propose a dynamic simulation framework for evaluating and improving persona-level role-playing in large language models (LLMs).
Outcome: The proposed framework leverages user-generated social content to construct a nuanced persona bank and elicits multi-turn, context-rich interactions within simulated social environments.
PersonaX: A Recommendation Agent-Oriented User Modeling Framework for Long Behavior Sequence (2025.findings-acl)

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Challenge: Existing methods for user profile modeling extract only partial segments from full historical behavior sequence, resulting in incomplete modeling and suboptimal profiling.
Approach: They propose an agent-agnostic LLM-UM framework to augment downstream recommendation agents . it segments complete historical behaviors into clustered groups and performs offline multi-persona profiling .
Outcome: The proposed framework improves agent performance and inference efficiency by 31% and 10% using 30–50% of behavioral data.
We Are What We Repeatedly Do: Inducing and Deploying Habitual Schemas in Persona-Based Responses (2023.emnlp-main)

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Challenge: a variety of personas can be elicited from large language models, but they are opaque and unpredictable.
Approach: They propose an approach to dialogue generation that retrieves relevant schemas to condition a large language model to generate persona-based responses.
Outcome: The proposed method captures habitual knowledge and generates persona-based responses from a large language model.

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