MoralBench: Moral Evaluation of LLMs

作者：Jianchao Ji, Yutong Chen, Mingyu Jin, Wujiang Xu, Wenyue Hua, Yongfeng Zhang · 发表于：ACM SIGKDD Explorations Newsletter · 年份：2025 · DOI：10.1145/3748239.3748246 · 被引用次数：14 · 研究领域：Ethics in Business and Education、Psychology of Moral and Emotional Judgment、Ethics in medical practice

In the rapidly evolving field of artificial intelligence, large language models (LLMs) have emerged as powerful tools for a myriad of applications, from natural language processing to decision-making support systems. However, as these models become increasingly integrated into societal frameworks, the imperative to ensure they operate within ethical and moral boundaries has never been more critical. This paper introduces a novel benchmark designed to measure and compare the moral reasoning capabilities of LLMs. We present the first comprehensive dataset specifically curated to probe the moral dimensions of LLM outputs, addressing a wide range of ethical dilemmas and scenarios reflective of real-world complexities. The main contribution of this work lies in the development of benchmark datasets and metrics for assessing the moral identity of LLMs, which accounts for nuance, contextual sensitivity, and alignment with human ethical standards. We publicly release the benchmark datasets1 and also open-source the code of the project2.