小语言模型代理的情绪敏感决策研究
On Emotion-Sensitive Decision Making of Small Language Model Agents
AI总结：
本文研究小语言模型在情绪影响下的决策机制，通过结合情绪诱导与结构化博弈评估，发现情绪扰动系统影响战略选择，但行为不稳定且不完全符合人类预期。
摘要：
小语言模型（SLM）越来越多地被用作交互决策代理，但大多数面向决策的评估忽略了情绪作为影响行为的因果因素。我们通过结合表示层面的情绪诱导与结构化博弈理论评估，研究情绪敏感决策。情绪状态通过激活引导技术从经过验证的真实世界情绪诱发文本中诱导，实现超出提示方法的可控和可迁移干预。我们围绕经典的决策模板构建了基准，这些模板涵盖了在完全和不完全信息下的合作与竞争激励。这些模板通过战略场景从Diplomacy、StarCraft II和多样化真实世界人物中实例化。跨多个模型家族在各种架构和模态上的实验表明，情绪扰动系统影响战略选择，但结果行为往往不稳定且不完全符合人类预期。最后，我们提出了一种改进情绪驱动扰动鲁棒性的方法。
英文摘要：
Small language models (SLM) are increasingly used as interactive decision-making agents, yet most decision-oriented evaluations ignore emotion as a causal factor influencing behavior. We study emotion-sensitive decision making by combining representation-level emotion induction with a structured game-theoretic evaluation. Emotional states are induced using activation steering derived from crowd-validated, real-world emotion-eliciting texts, enabling controlled and transferable interventions beyond prompt-based methods. We introduce a benchmark built around canonical decision templates that span cooperative and competitive incentives under both complete and incomplete information. These templates are instantiated using strategic scenarios from \textsc{Diplomacy}, \textsc{StarCraft II}, and diverse real-world personas. Experiments across multiple model families in various architecture and modalities, show that emotional perturbations systematically affect strategic choices, but the resulting behaviors are often unstable and not fully aligned with human expectations. Finally, we outline an approach to improve robustness to emotion-driven perturbations.
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。
