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大型语言模型在道德判断中的稳定性

Soyoung Oh¹, Vera Demberg¹

¹Department of Computer Science, Language Science and Technology, Saarland University, Saarbrücken, Germany.

Royal Society open science

|April 24, 2025

概括

关于大语言模型 (LLM) 和道德的先前研究是不可靠的. 我们的研究表明,LLM的道德判断对任务设计和提示措辞敏感,使以前的结论无效.

科学领域:

人工智能的人工智能
伦理学伦理学伦理学
自然语言处理自然语言处理.

背景情况:

越来越多的人对大语言模型 (LLM) 了解道德感兴趣.
之前的研究已经在道德判断任务上测试了LLM,这表明了类似人类的对齐.
有关用于评估LLM道德推理的方法存在担忧.

研究的目的:

批判性地评估LLM道德判断的先前研究的方法和结果.
调查任务呈现和快速制定对LLM响应的影响.
为更强大的未来研究设计提供建议.

主要方法:

分析现有的方法来从LLMs中获得道德判断.
测试LLM响应对任务选项呈现变化的敏感性.
检查微妙的提示措辞变化对LLM道德判断的影响.

主要成果:

之前的LLM道德判断研究被陈述偏见所混.
在LLM的响应中,对微小的快速配方变化有很高的敏感性.
从LLM道德对齐的先前工作中得出的结论没有得到支持.

结论:

评估LLM道德判断的现有方法缺乏有效性.
伦理理学士道德推理非常容易受到实验设计工件的影响.
未来的研究需要更严格和有偏见的方法来准确评估LLM的道德能力.

关键词:

大型语言模型道德推理道德推理坚固性坚固性坚固性

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