让大模型更符合人类风险偏好，提升决策可信度
Evaluating and Aligning Human Economic Risk Preferences in LLMs
- 基于角色设定测试大模型风险态度
- 复杂任务中模型表现下降，需对齐人类理性
- 提出新对齐方法，提升模型经济决策合理性
大型语言模型（LLMs）在涉及风险评估的决策场景中应用日益广泛，但其与人类经济理性的对齐程度仍不明确。本研究探究了不同人格设定下，LLM 是否表现出符合人类预期的风险偏好。具体而言，我们评估了模型生成回答是否能体现个体角色所对应的适当风险规避或风险偏好行为。结果表明，尽管在简化、个性化的风险情境中模型能做出合理决策，但在更复杂的经济决策任务中其表现下降。为此，我们提出一种对齐方法，旨在增强模型对特定角色风险偏好的遵循能力。该方法显著提升了大模型在风险相关应用中的经济理性，为实现更贴近人类的AI决策迈出了重要一步。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly used in decision-making scenarios that involve risk assessment, yet their alignment with human economic rationality remains unclear. In this study, we investigate whether LLMs exhibit risk preferences consistent with human expectations across different personas. Specifically, we assess whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual's persona. Our results reveal that while LLMs make reasonable decisions in simplified, personalized risk contexts, their performance declines in more complex economic decision-making tasks. To address this, we propose an alignment method designed to enhance LLM adherence to persona-specific risk preferences. Our approach improves the economic rationality of LLMs in risk-related applications, offering a step toward more human-aligned AI decision-making.
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