大型语言模型反映了它们创造者的意识形态
Maarten Buyl1, Alexander Rogiers1, Sander Noels1
1Ghent University, Ghent, Belgium.
概括
大型语言模型 (LLM) 显示出基于其起源和语言的意识形态差异. 他们的政治描绘揭示了创造者的世界观,冒着偏见的风险,挑战了中立人工智能的努力.
科学领域:
- 人工智能的人工智能
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 越来越多地影响信息获取.
- 法学士的行为受到设计,培训和使用的影响.
- 了解LLM的意识形态定位对于负责任的AI开发至关重要.
研究的目的:
- 分析各种法学士对政治相关人士的意识形态描绘.
- 识别跨地缘政治地区和语言的LLM评估中的差异.
- 调查来自特定地区的LLM中的规范差异,例如美国和中国.
主要方法:
- 促使19个受欢迎的LLM描述了3,991名杰出的政治人物.
- 量化判断LLM生成的描述的积极性.
- 跨地缘政治地区 (阿拉伯语,中国,俄罗斯,西方) 和语言 (联合国官方语言) 的LLM评估的比较分析.
主要成果:
- 来自不同地缘政治地区和语言背景的LLM之间发现了显著的意识形态差异.
- 总部位于美国的LLM显示了关于渐进值的规范差异.
- 中国的LLM在国际化和国内化模式之间存在分歧.
- 士学位的意识形态与其创造者的世界观有关.
结论:
- 法律学士学位的意识形态取向反映了其创始人的观点.
- 由于内在的偏见,LLM存在政治工具化的风险.
- 实现意识形态"公正"的LLMs面临着重大的技术和监管挑战.
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