arXiv cs.AI· Jianchao Ji, Yutong Chen, Mingyu Jin, Wujiang Xu, Wenyue Hua, Yongfeng Zhang·· 3 小时前AI 评分22
MoralBench：面向 LLM 的道德评测基准
MoralBench: Moral Evaluation of LLMs
AI 导读
研究者提出 MoralBench，这是首个专门用于衡量和比较大语言模型道德推理能力的综合评测数据集，覆盖反映现实复杂性的多种伦理困境与场景。该方法结合定量分析与伦理学者的定性见解，评估模型的细致性、情境敏感度及与人类伦理标准的对齐。对多个主流 LLM 的测试显示不同模型的道德推理能力差异显著，基准与代码已开源发布，论文被 ACM SIGKDD Explorations 接收。
正文 · 原文
Abstract: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. Our methodology involves a multi-faceted approach, combining quantitative analysis with qualitative insights from ethics scholars to ensure a thorough evaluation of model performance. By applying our benchmark across several leading LLMs, we uncover significant variations in moral reasoning capabilities of different models. These findings highlight the importance of considering moral reasoning in the development and evaluation of LLMs, as well as the need for ongoing research to address the biases and limitations uncovered in our study. We publicly release the benchmark at this https URL and also open-source the code of the project at this https URL.
| Comments: | Accepted to ACM SIGKDD Explorations Volume 27 Issue 1 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2406.04428 [cs.CL] |
| (or arXiv:2406.04428v3 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2406.04428 arXiv-issued DOI via DataCite |
Submission history
From: Jianchao Ji [view email]
[v1]
Thu, 6 Jun 2024 18:15:01 UTC (1,518 KB)
[v2]
Fri, 4 Jul 2025 03:19:53 UTC (1,460 KB)
[v3]
Wed, 11 Nov 2026 21:25:07 UTC (1,453 KB)
来源：arXiv cs.AI · arxiv.org