概括
大多数对话型大语言模型 (LLM) 显示左中心的政治偏见. 这种偏见可以故意使用监督微调 (SFT) 嵌入,随着LLM获得影响力,这引发了社会关注.
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) 越来越多地被用作信息来源.
- 了解LLM中的潜在偏见对于负责任的AI开发和部署至关重要.
- 人工智能系统中的政治偏见可以显著影响公共话语和决策.
研究的目的:
- 综合分析嵌入在最先进的对话型大语言模型 (LLM) 中的政治偏好.
- 通过微调技术,调查政治取向是否可以在LLMs中被故意操纵.
- 评估政治偏见在LLMs中的社会影响.
主要方法:
- 对24个对话式LLM (闭源和开源) 进行了11个不同的政治导向测试.
- 基础模型对微调对话模型的评估反应.
- 利用监督微调 (SFT) 与政治上一致的数据来指导LLM的方向.
主要成果:
- 大多数对话型LLM在测试时表现出左中心的政治偏好.
- 基础模型由于测试性能差,显示了不确定的结果.
- 监督微调 (SFT) 证明了在有限数据的LLM中嵌入特定政治方向的能力.
结论:
- 交谈式的LLM通常表现出左中心的政治倾向.
- 通过监督微调 (SFT),LLM容易受到政治操纵.
- 在LLM中潜在的嵌入政治偏见需要仔细考虑其社会影响.
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