用大型语言模型评估政治微观定位的说服力影响
Kobi Hackenburg1, Helen Margetts1
1Oxford Internet Institute, University of Oxford, Oxford OX1 2JD, United Kingdom.
概括
大型语言模型 (LLM) 可以说服选民,但个性化微型定向在一项大型实验中没有超过通用消息. 法律法规的影响可能来自于它们的一般信息,而不是个别定制.
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) 提供了自动化政治微观定位的潜力.
- 由LLM驱动的微型定位的实际说服力影响在经验上仍未得到充分探索.
研究的目的:
- 调查LLM产生的微目标政治信息的说服力影响.
- 为了比较个性化与非个性化的LLM信息的有效性.
主要方法:
- 开发了一个 Web 应用程序,将用户数据集成到 GPT-4 提示中,以便实时生成消息.
- 进行了一项预先注册的随机对照实验,涉及四个政治问题,共有8587名参与者.
主要成果:
- GPT-4产生了广泛的有说服力的信息,将议题立场支持率提高了12个百分点.
- 与非微型目标信息相比,微型目标信息在说服力影响上没有统计学上显著的差异 (4.83%对6.20%).
- 这些发现适用于定制的数据中存在差异.
结论:
- 目前的LLM的说服力可能在于它们的通用信息质量,而不是个体级别的定制.
- 该研究提供了GPTarget2024数据集,作为未来对LLM政治影响力研究的基准.
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