利用大语言模型解决伦理决策中的道德不确定性问题
Addressing Moral Uncertainty using Large Language Models for Ethical Decision-Making
AI总结：
本研究提出一种伦理决策框架，通过任务无关伦理层结合LLM生成的多道德视角反馈微调RL模型，利用信念聚合方法生成塑形奖励，帮助智能体应对道德不确定性，提升决策一致性与适应性，减少对人工伦理奖励的依赖。
摘要：
我们提出了一种伦理决策框架，该框架通过一个任务无关的伦理层来优化预训练的强化学习（RL）模型。在初始训练后，RL模型会进行伦理微调，此过程中人类反馈被大语言模型（LLM）生成的反馈所替代。该LLM将结果论、义务论、美德伦理、社会正义伦理与关怀伦理作为道德原则，在伦理决策过程中为推荐动作分配信念值。伦理层利用信念詹森-香农散度（Belief Jensen-Shannon Divergence）与Dempster-Shafer理论，将来自多个LLM衍生道德视角的信念得分聚合为概率得分，这些得分同时也作为塑形奖励，引导智能体做出符合平衡伦理框架的选择。这种集成学习框架帮助RL智能体在复杂环境中应对道德不确定性，使其能够在各类任务中做出符合道德的决策。我们在不同LLM变体上测试了该方法，并与其他信念聚合技术进行对比，结果显示该方法提升了一致性与适应性，且降低了对人工设计伦理奖励的依赖。该方法在伦理挑战意外出现的动态场景中尤为有效，因此非常适用于现实世界应用。
英文摘要：
We present an ethical decision-making framework that refines a pre-trained reinforcement learning (RL) model using a task-agnostic ethical layer. Following initial training, the RL model undergoes ethical fine-tuning, where human feedback is replaced by feedback generated from a large language model (LLM). The LLM embodies consequentialist, deontological, virtue, social justice, and care ethics as moral principles to assign belief values to recommended actions during ethical decision-making. An ethical layer aggregates belief scores from multiple LLM-derived moral perspectives using Belief Jensen-Shannon Divergence and Dempster-Shafer Theory into probability scores that also serve as the shaping reward, steering the agent toward choices that align with a balanced ethical framework. This integrated learning framework helps the RL agent navigate moral uncertainty in complex environments and enables it to make morally sound decisions across diverse tasks. Our approach, tested across different LLM variants and compared with other belief aggregation techniques, demonstrates improved consistency, adaptability, and reduced reliance on handcrafted ethical rewards. This method is especially effective in dynamic scenarios where ethical challenges arise unexpectedly, making it well-suited for real-world applications.
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。
