理性之光：推理大语言模型是否与人类判断和选择一致？
Sparks of Rationality: Do Reasoning LLMs Align with Human Judgment and Choice?
AI总结：
研究评估了LLMs在理性选择和情感影响下的表现，发现有意识思考能提升理性，但情感引导方法在可控性与人类行为对齐间存在权衡。
摘要：
大语言模型（LLMs）正越来越多地被定位为招聘、医疗和经济判断的决策引擎，但现实中的人类判断反映的是理性推理与情绪驱动偏见之间的平衡。如果LLMs要参与高风险决策或作为人类行为的模型，就必须评估它们是否表现出类似的理性或偏见模式。为此，我们评估了多个LLM家族在（i）测试理性选择核心公理的基准测试以及（ii）来自行为经济学和社会规范的经典决策领域中的表现，其中情绪已知会影响判断和选择。在各种设置中，我们发现有意识的“思考”能够可靠地提高理性，并推动模型向期望值最大化发展。为了探测人类般的情感扭曲及其与推理的相互作用，我们使用了两种情绪引导方法：上下文引导（ICP）和表示层面引导（RLS）。ICP诱导出强烈的定向转变，通常极端且难以校准，而RLS则产生更符合心理的模式，但可靠性较低。我们的结果表明，促进理性的相同机制也放大了对情感干预的敏感性，并且不同的引导方法在可控性与人类对齐行为之间进行权衡。总体而言，这揭示了推理与情感引导之间的张力，对人类模拟和LLM决策系统的安全部署具有启示意义。
英文摘要：
Large Language Models (LLMs) are increasingly positioned as decision engines for hiring, healthcare, and economic judgment, yet real-world human judgment reflects a balance between rational deliberation and emotion-driven bias. If LLMs are to participate in high-stakes decisions or serve as models of human behavior, it is critical to assess whether they exhibit analogous patterns of (ir)rationalities and biases. To this end, we evaluate multiple LLM families on (i) benchmarks testing core axioms of rational choice and (ii) classic decision domains from behavioral economics and social norms where emotions are known to shape judgment and choice. Across settings, we show that deliberate "thinking" reliably improves rationality and pushes models toward expected-value maximization. To probe human-like affective distortions and their interaction with reasoning, we use two emotion-steering methods: in-context priming (ICP) and representation-level steering (RLS). ICP induces strong directional shifts that are often extreme and difficult to calibrate, whereas RLS produces more psychologically plausible patterns but with lower reliability. Our results suggest that the same mechanisms that improve rationality also amplify sensitivity to affective interventions, and that different steering methods trade off controllability against human-aligned behavior. Overall, this points to a tension between reasoning and affective steering, with implications for both human simulation and the safe deployment of LLM-based decision systems.
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