大型语言模型的个性测试:时间稳定性有限,但强调了亲社会性
Bojana Bodroža1, Bojana M Dinić1, Ljubiša Bojić2,3
1Department of Psychology, Faculty of Philosophy, University of Novi Sad, Novi Sad, Serbia.
Royal Society open science
|October 10, 2024
概括
大型语言模型 (LLM) 在人格反应中表现出不同的稳定性. 虽然Llama3和GPT-4o等一些LLM表现出更高的同意,但大多数显示出社会上可取和亲社会人格的个人资料.
科学领域:
- 人工智能的人工智能
- 心理学 心理学 心理学
- 人与计算机的交互
背景情况:
- 大型语言模型 (LLM) 越来越普遍,需要研究它们对社会的影响.
- 了解LLM行为,包括人格模拟,对于AI安全和发展至关重要.
研究的目的:
- 评估 LLM 对个性仪器的响应的时间稳定性和评分间一致性.
- 分析和比较LLM人格特征与人类规范性数据.
主要方法:
- 在两个时间点调查了七个不同的LLM.
- 利用个性工具来评估响应的一致性和一致性.
- 与已建立的人类数据对比LLM人格特征.
主要成果:
- 在LLM中,评价者间的一致性不同,Llama3和GPT-4o显示出比GPT-4和Gemini更高的一致性.
- 协议受评估的特定人格工具和特征的影响.
- 总的来说,法学士在社会上具有可取的特征,具有高度的愉悦性和认真性,低的马基雅维利主义.
结论:
- 在模拟稳定的个性特征方面,LLM表现出可变的稳定性.
- 一致的个性反应对于人工智能系统至关重要,因为它们的社会整合和安全影响.
- 需要进一步的研究来提高LLM人格模拟的可靠性和可预测性.
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