Making moral judgments is an essential step toward developing ethical AI
systems. Prevalent approaches are mostly implemented in a bottom-up manner,
which uses a large set of annotated data to train models based on crowd-sourced
opinions about morality. These approaches have been criticized for potentially
overgeneralizing a limited group of annotators' moral stances and lacking
explainability. In contrast, top-down approaches make moral judgments grounded
in a set of principles. However, it remains conceptual due to the incapability
of previous language models and the unsolved debate among moral principles. In
this study, we propose a flexible framework to steer Large Language Models
(LLMs) to perform moral reasoning with well-established moral theories from
interdisciplinary research. The theory-guided top-down framework can
incorporate various moral theories. Our experiments demonstrate the
effectiveness of the proposed framework on datasets derived from moral
theories. Furthermore, we show the alignment between different moral theories
and existing morality datasets. Our analysis exhibits the potentials and flaws
in existing resources (models and datasets) in developing explainable moral
judgment-making systems.

提出了一个灵活的框架，利用跨学科研究中建立的道德理论，引导大型语言模型进行道德推理，证明了该框架在从道德理论中衍生的数据集上的有效性，展示了不同道德理论与现有道德数据集之间的一致性，并展示了开发可解释的道德判断系统中现有资源（模型和数据集）的潜力和缺陷。