RoleCDE：角色扮演代理中的角色-对齐权衡的基准测试与缓解
RoleCDE:Benchmarking and Mitigating Role-Alignment Trade-offs in Role-Playing Agents
AI总结：
针对角色扮演代理在角色特定价值与对齐约束冲突时的决策问题，提出首个基准RoleCDE，通过认知困境场景评估角色-场景基础、价值冲突解决和决策倾向，发现“角色价值解耦”现象，并基于RoleCDE的微调有效缓解该问题。
摘要：
角色扮演代理（RPAs）被广泛用于引导大语言模型（LLMs）表现出角色一致的行为，然而现有基准主要评估表面保真度，对角色-对齐价值冲突下的决策提供有限洞察。为解决这一差距，我们引入RoleCDE，这是首个旨在评估RPAs在角色特定价值与对齐导向约束之间结构化冲突下的基准。RoleCDE将角色感知决策制定为认知困境场景，联合评估角色-场景基础、价值冲突解决和决策倾向。该基准大规模构建，涵盖约8000个多样化的角色档案和场景，以及近24000个困境实例，跨越三个难度级别和八个角色类别。对几个主流LLMs的评估揭示了一种“角色价值解耦”现象，即当两者冲突时，代理系统性地默认选择对齐和道德一致的决策，而非角色特定价值，即使在明确的角色条件下也是如此。这种行为在很大程度上不受困境难度影响，但在不同角色类别间差异显著。我们进一步表明，基于RoleCDE的微调通过改善价值权衡推理有效缓解了这种解耦，同时保持了通用角色扮演保真度和通用推理性能。代码可在 https://github.com/rabbitrose/RoleCDE 获取。
英文摘要：
Role-playing agents(RPAs) are widely used to steer large language models(LLMs) toward role-consistent behavior, yet existing benchmarks mainly evaluate surface-level fidelity and offer limited insight into decision making under role-alignment value conflicts. To address this gap, we introduce RoleCDE, the first benchmark designed to evaluate RPAs under structured conflicts between role-specific values and alignment-oriented constraints. RoleCDE formulates role-aware decision making as cognitive dilemma scenarios, jointly evaluating role-scenario grounding, value conflict resolution, and decision tendencies. The benchmark is constructed at scale, covering approximately 8k diverse role profiles and scenarios and nearly 24k dilemma instances across three difficulty levels and eight role categories. Evaluation of several mainstream LLMs reveals a "Role Value Decoupling" phenomenon, where agents systematically default to alignment-and morality-consistent decisions rather than role-specific values when the two conflict, even under explicit role conditioning. This behavior is largely invariant to dilemma difficulty but varies substantially across role categories. We further show that RoleCDE-based fine-tuning effectively mitigates this decoupling by improving value trade-off reasoning, while preserving general role-playing fidelity and general reasoning performance. Code is available at: https://github.com/rabbitrose/RoleCDE.
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