伦理阶梯：通过多步归纳到复杂道德困境探究大语言模型价值优先级
The Staircase of Ethics: Probing LLM Value Priorities through Multi-Step Induction to Complex Moral Dilemmas
AI总结：
针对现有单步评估的不足，本文提出多步道德困境数据集（MMDs），通过3,302个五阶段困境动态评估九个LLM的道德推理，发现其价值偏好随情境演变，呼吁转向动态评估范式。
摘要：
伦理决策是人类判断的一个关键方面，而大语言模型（LLMs）在决策支持系统中的日益广泛应用，要求对其道德推理能力进行严格评估。然而，现有评估主要依赖单步评估，未能捕捉模型如何适应不断演变的伦理挑战。针对这一空白，我们引入了多步道德困境（MMDs），这是首个专门构建的数据集，用于评估大语言模型在3,302个五阶段困境中的道德判断演变。该框架能够对大语言模型在逐步升级的困境中如何调整其道德推理进行细粒度、动态的分析。我们对九个广泛使用的大语言模型的评估显示，随着困境的推进，它们的价值偏好发生显著变化，表明模型会根据场景复杂性重新校准道德判断。此外，成对价值比较表明，尽管大语言模型通常优先考虑关怀价值，但在某些情境下，这一价值有时会被公平性所超越，凸显了大语言模型伦理推理的动态性和情境依赖性。我们的研究结果呼吁转向动态、情境感知的评估范式，为大语言模型更符合人类价值观和更注重价值敏感性的发展铺平道路。
英文摘要：
Ethical decision-making is a critical aspect of human judgment, and the growing use of LLMs in decision-support systems necessitates a rigorous evaluation of their moral reasoning capabilities. However, existing assessments primarily rely on single-step evaluations, failing to capture how models adapt to evolving ethical challenges. Addressing this gap, we introduce the Multi-step Moral Dilemmas (MMDs), the first dataset specifically constructed to evaluate the evolving moral judgments of LLMs across 3,302 five-stage dilemmas. This framework enables a fine-grained, dynamic analysis of how LLMs adjust their moral reasoning across escalating dilemmas. Our evaluation of nine widely used LLMs reveals that their value preferences shift significantly as dilemmas progress, indicating that models recalibrate moral judgments based on scenario complexity. Furthermore, pairwise value comparisons demonstrate that while LLMs often prioritize the value of care, this value can sometimes be superseded by fairness in certain contexts, highlighting the dynamic and context-dependent nature of LLM ethical reasoning. Our findings call for a shift toward dynamic, context-aware evaluation paradigms, paving the way for more human-aligned and value-sensitive development of LLMs.
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。
