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来自中国的大型语言模型中的政治审查

Jennifer Pan¹, Xu Xu²

¹Department of Communication, Stanford University, 450 Jane Stanford Way, Stanford, CA 94305, USA.

|February 19, 2026

概括

政府的审查可能会导致大型语言模型 (LLM) 的政治偏见. 来自中国的LLM显示出比非中国模型更多的审查,这表明监管压力会影响AI的政治偏见.

科学领域:

人工智能的人工智能
自然语言处理自然语言处理.
技术对社会的影响技术对社会的影响

背景情况:

大型语言模型 (LLM) 经常表现出反映社会模式的偏见.
在LLMs中,一个较少被探索的偏见来源是政府的审查.
了解审查的影响对于人工智能开发和部署至关重要.

研究的目的:

调查政府审查对大型语言模型 (LLM) 的影响.
为了比较在中国开发的LLMs中的政治偏见与其他地方开发的LLMs.
为了区分审查偏见与其他潜在的LLM偏见来源.

主要方法:

来自中国和非中国地区的基础模型的比较分析.
通过对不同敏感度级别的145个政治问题进行评估.
评估响应率,响应长度和响应准确度.

主要成果:

与非中国模型相比,来自中国的LLM表现出明显更高的拒绝率,更短的响应,以及政治提示的不准确性增加.
对于政治上不那么敏感的提示,差异有所减少,这表明审查是关键因素.
虽然语言 (中文与英语) 影响了回应,但LLM的起源 (中国与非中国) 对审查偏见的影响更为明显.

结论:

政府的审查,由限制政治内容的监管压力驱动,似乎是LLMs政治偏见的重要贡献者.
观察数据表明,人工智能模型中的监管环境和审查行为之间存在相关性.
需要进一步的研究来建立因果关系,但研究结果强调了人工智能开发中关于政治内容调节的透明度的需要.

关键词:

在这里,我们可以看到AIAIAI.中国中国中国中国.在法学士 (LLM) 课程中.审查审查审查政治偏见的政治偏见

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