语言模型尺寸的缩放产生了单一信息政治说服力的收益下降
Kobi Hackenburg1,2, Ben M Tappin3, Paul Röttger4
1Oxford Internet Institute, University of Oxford, Oxford OX1 2JD, United Kingdom.
概括
较大的语言模型 (LLM) 显示,政治信息的说服力回报正在下降. 任务完成,而不是大小,似乎是LLM有效性的关键因素.
科学领域:
- 人工智能的人工智能
- 政治科学 政治科学是指政治学.
- 计算社会科学 计算社会科学
背景情况:
- 大型语言模型 (LLM) 展示了人类层面的政治信息生成能力.
- 人们担心,随着模型大小的增加,LLM的说服力越来越强.
研究的目的:
- 研究语言模型大小与生成的政治信息的说服力之间的关系.
- 为了确定较大的模型是否比较小的模型具有显著的说服力优势.
主要方法:
- 通过使用不同尺寸的24种语言模型,在10个美国政治问题上生成了720条政治信息.
- 进行了一项大规模的随机调查实验,参与了25982名参与者,以评估信息的说服力.
- 分析了模型大小和任务完成指标 (连贯性,时事性) 对说服力的影响.
主要成果:
- 模型的说服力表现出大幅下降的回报;边境模型在显著较小的模型上只提供边际收益.
- 在调整任务完成时,LLM大小和说服力之间的相关性在统计学上变得微不足道.
- 较大的模型的说服力优势可能通过任务完成进行调解,当前的前沿模型接近最高性能.
结论:
- 进一步扩大语言模型大小可能不会大大提高静态政治信息的说服力.
- 任务完成指标对于理解和潜在地提高LLM产生的内容有效性至关重要.
- 这些发现表明,扩大LLM的政治信息传递的说服能力可能会停滞不前.
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