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：大语言模型在摘要生成、算术推理和问答等任务中展现出卓越能力，但在道德推理和伦理决策领域仍面临重大挑战，尤其当涉及多利益相关方的复杂场景时。本文提出"身临其境"框架，旨在通过从多利益相关方视角探索决策后果来增强大语言模型的道德推理能力。该框架的核心机制在于模拟行为问责制，并辅以共情训练与风险评估，这些要素对其有效性至关重要。我们通过专有与开源大语言模型在多项道德推理基准上验证了SKIG的性能，并通过广泛的消融分析深入探究了其关键组成。