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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 5 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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A role-playing LLM that reasons about the scene and its own state before responding, trained with two semantic rewards, beats stronger baselines on role-play benchmarks.
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Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning
RAR improves role-playing agents by distilling character-grounded reasoning traces and optimizing the reasoning style to fit the dialogue scene.
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H2HTalk: Evaluating Large Language Models as Emotional Companion
H2HTalk is a new 4,650-scenario benchmark that scores LLM emotional companions on dialogue, memory, and itinerary planning, and finds models struggle with implicit needs and long-horizon memory.
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