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Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?
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Character is Destiny: Can Role-Playing Language Agents Make Persona-Driven Decisions?
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Can Large Language Models (LLMs) simulate humans in making important decisions? Recent research has unveiled the potential of using LLMs to develop role-playing language agents (RPLAs), mimicking mainly the knowledge and tones of various characters. However, imitative decision-making necessitates a more nuanced understanding of personas. In this paper, we benchmark the ability of LLMs in persona-driven decision-making. Specifically, we investigate whether LLMs can predict characters' decisions provided by the preceding stories in high-quality novels. Leveraging character analyses written by literary experts, we construct a dataset LIFECHOICE comprising 1,462 characters' decision points from 388 books. Then, we conduct comprehensive experiments on LIFECHOICE, with various LLMs and RPLA methodologies. The results demonstrate that state-of-the-art LLMs exhibit promising capabilities in this task, yet substantial room for improvement remains. Hence, we further propose the CHARMAP method, which adopts persona-based memory retrieval and significantly advances RPLAs on this task, achieving 5.03% increase in accuracy.
Forward citations
Cited by 11 Pith papers
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The Story Shapes the Agent: Narrative Priors in LLM Behavior
Task narrative, not persona, is the dominant driver of LLM agent action profiles in structurally identical investigation games, and transferable personas are those with concrete action words.
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Improving General Role-Playing Agents via Psychology-Grounded Reasoning and Role-Aware Policy Optimization
Psy-CoT decomposes reasoning into Interaction Perception, Psychological Empathy, and Logical Construction while RAPO asymmetrically weights role-specific tokens during policy optimization, outperforming prior CoT and ...
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Reinforcing Human Behavior Simulation via Verbal Feedback
DITTO uses RL with verbal feedback to train LLMs for human behavior simulation, reporting 36% average gains over base models and outperforming GPT-5.4 on 6 of 10 SOUL benchmark tasks.
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Through the Lens of Character: Resolving Modality-Role Interference in Multimodal Role-Playing Agent
CAVI framework uses character-guided token pruning, orthogonal feature modulation, and modality-adaptive role steering to resolve modality-role interference in multimodal RPAs.
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Moral Susceptibility and Robustness under Persona Role-Play in Large Language Models
LLM moral robustness under persona role-play is largely determined by model family with Claude models most consistent, while susceptibility shows little family dependence.
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Synthia: Scalable Grounded Persona Generation from Social Media Data
Synthia creates scalable personas from Bluesky posts that better match human survey responses than prior methods, uses smaller models, and retains social network structure for network-aware analysis.
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Creating and Evaluating Personas Using Generative AI: A Scoping Review of 81 Articles
A scoping review of 81 articles finds generative AI widely applied to persona development with 61% resource sharing but 45% lacking evaluation and frequent GPT-only use, proposing guidelines to address circularity and...
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Adaptive Turn-Taking for Real-time Multi-Party Voice Agents
ModeratorLM conditions a streaming speech LLM on assigned roles for adaptive turn-taking in multi-party settings, reporting over 40% higher precision and 70% higher recall than non-role baselines on real meetings and ...
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Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation
Introduces PAS and FAS task abstractions plus the LLM-S^3 benchmark to evaluate LLMs on generating sociodemographic survey responses across 11 real datasets and multiple models.
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Adaptive Turn-Taking for Real-time Multi-Party Voice Agents
ModeratorLM conditions a chunk-wise streaming speech LLM on assigned roles (with optional CoT) to raise turn-taking precision over 40% and recall over 70% versus non-role baselines on synthetic RolePlayConv data and r...
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Inertia in Moral and Value Judgments of Large Language Models
LLMs exhibit persistent inertia in value orientations, with harm avoidance and fairness remaining skewed across persona prompts.
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