Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering. However, they encounter significant challenges in the domain of moral reasoning and ethical decision-making, especially in complex scenarios with multiple stakeholders. This paper introduces the Skin-in-the-Game (SKIG) framework, aimed at enhancing moral reasoning in LLMs by exploring decisions' consequences from multiple stakeholder perspectives. Central to SKIG's mechanism is simulating accountability for actions, which, alongside empathy exercises and risk assessment, is pivotal to its effectiveness. We validate SKIG's performance across various moral reasoning benchmarks with proprietary and opensource LLMs, and investigate its crucial components through extensive ablation analyses.

翻译：大语言模型（LLMs）在摘要生成、算术推理和问答等任务中展现出卓越能力。然而，在道德推理与伦理决策领域，尤其是在涉及多利益相关方的复杂场景中，它们仍面临显著挑战。本文提出"利益攸关"（Skin-in-the-Game，SKIG）框架，旨在通过从多利益相关方视角探究决策后果来增强大语言模型的道德推理能力。该机制的核心在于模拟行为问责机制，结合共情训练与风险评估，共同构成其有效性的关键支柱。我们在多个道德推理基准上使用专有及开源大语言模型验证了SKIG的性能，并通过系统的消融实验深入探究了其核心组件的贡献。