生成性语言模型表现出社会身份偏见
Tiancheng Hu1, Yara Kyrychenko2, Steve Rathje3
1Department of Theoretical and Applied Linguistics, University of Cambridge, Cambridge, UK. th656@cam.ac.uk.
Nature computational science
|December 12, 2024
概括
大型语言模型 (LLM) 显示了社会身份偏见,有利于他们的"内部群体",并抵制"外部群体",类似于人类. 有针对性的数据策划和微调可以减少这些AI偏见.
科学领域:
- 人工智能的人工智能
- 社会心理学 社会心理学
- 人与计算机的交互
背景情况:
- 社会认同偏见,包括群内偏好和群外敌意,在人类心理学中得到了很好的记录.
- 人工智能系统中这种偏差的存在和程度,特别是大型语言模型 (LLM),仍然在很大程度上未被探索.
研究的目的:
- 调查大型语言模型 (LLM) 是否表现出与人类模式相似的社会身份偏见.
- 评估在各种LLM架构和培训范式中对内组偏好和外组豁免的普遍性.
主要方法:
- 对77种不同的大型语言模型 (LLM) 进行了句子完成提示 (例如",我们是"...).
- 在受控实验环境和自然的人类-LLM对话中评估了偏见的表现.
- 研究了培训数据策划和专门微调对偏差水平的影响.
主要成果:
- 几乎所有基本的LLM和一些指令/偏好调整模型都显示出显著的内组偏好和外组豁免.
- 这些社会身份偏见在不同的实验条件和相互作用类型中被观察到.
- 经过仔细的培训数据策划和微调策略,发现可以大大减轻LLMs中的这些偏见.
结论:
- 大型语言模型 (LLM) 本质上具有社会身份偏见,反映了人类的心理倾向.
- 虽然偏见很普遍,但它们可以通过数据和模型培训的有针对性的干预来有效地减少.
- 了解和减轻人工智能社会偏见对于开发公平的人工智能和防止通过人类-人工智能互动加强社会偏见至关重要.
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