通过动态重要性估计增强解码时的个性跟随以用于角色扮演代理
Enhancing Persona Following at Decoding Time via Dynamic Importance Estimation for Role-Playing Agents
AI总结：
本文提出了一种基于理论的动态重要性估计方法，通过加权奖励引导解码提升角色扮演代理在动态场景中的个性跟随能力。
摘要：
角色扮演语言代理在社会研究中的实用性随着大语言模型的采用而增长。为了在社会模拟中实现真实性，这些代理必须遵循由角色档案定义的个性，但现有策略——静态提示工程或昂贵的微调——无法适应动态场景。心理学理论，如认知-情感人格系统，提供了这一失败的关键解释：个性对行为的影响不是静态的，而是随着场景变化而变化。这种情境依赖性突显了对适应性个性管理的迫切需求。为了解决这一差距，我们提出了一种新的、基于理论的方法，能够动态估计情境相关的个性重要性，并将其整合到加权奖励引导的解码中，从而在推理时实现个性跟随。具体来说，我们引入了个性动态解码（PDD）框架，它包含两个关键组件：（1）个性重要性估计（PIE）模块，该模块能够在不需要真实监督的情况下动态量化个性属性的情境重要性；（2）个性引导的推理时对齐（PIA）范式，它利用这些重要性评分来构建加权多目标奖励，并在推理过程中调节生成概率。广泛的实验显示，我们的方法在话语一致性和行为忠实性方面都有效。
英文摘要：
The utility of Role-Playing Language Agents in sociological research is growing alongside the adoption of Large Language Models. For realism in social simulation, these agents must adhere to their personas defined by character profiles, yet existing strategies-static prompt engineering or costly fine-tuning-fail to adapt personas to dynamic scenarios. Psychological theories, such as the Cognitive-Affective Personality Systems, provide a crucial explanation for this failure: a persona's influence on behavior is not static but varies with the scenarios. This context-dependence highlights the critical need for adaptive persona management. To address this gap, we propose a novel, theory-driven method that dynamically estimates context-dependent persona importance and integrates it into weighted reward-guided decoding, enabling inference-time persona following. Specifically, we introduce the Persona Dynamic Decoding (PDD) framework, which consists of two key components: (1) Persona Importance Estimation (PIE) module, which dynamically quantifies the contextual importance of persona attributes without requiring ground-truth supervision; and (2) Persona-Guided Inference-Time Alignment (PIA) paradigm, which leverages these importance scores to construct weighted multi-objective rewards and modulate generation probabilities during inference. Extensive experiments show the effectiveness of our method in utterance consistency and behavioral fidelity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
