聊天机器人投票咨询应用程序提供信息,但很少影响年轻的无党派选民
Yamil R Velez1, Donald P Green1, Semra Sevi2
1Department of Political Science, Columbia University, New York, NY 10027.
概括
一个使用大型语言模型 (LLM) 的新投票咨询应用程序 (VAA) 机器人改善了年轻的,未加入的成年人的政治知识. 然而,它对那些具有强烈现有问题对齐的人的投票偏好的影响有限.
科学领域:
- 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
- 人与计算机的交互
背景情况:
- 投票咨询应用程序 (VAA) 在提高政治知识和参与方面的有效性方面没有得到充分的研究.
- 由于用户自选,传统的VAA可能无法接触到较少参与的选民.
- 大型语言模型 (LLM) 为个性化政治信息传递提供了新的可能性.
研究的目的:
- 引入和评估一个新的VAA机器人,利用LLMs和检索增强生成.
- 评估VAA Bot对年轻,政治无关的成年人政治知识和参与的影响.
- 了解基于LLM的工具在影响投票偏好和政党评估方面的局限性.
主要方法:
- 开发一个VAA机器人集成LLMs和检索增强生成.
- 进行了三项实验性研究,对年轻,政治上无关的成年人进行了研究.
- 收集关于用户知识,问题立场,投票偏好和党派评估的数据.
主要成果:
- VAA Bot显著提高了用户对个人重要问题的党派立场的了解.
- 观察到对投票偏好和党派评价的影响较弱,特别是对于具有强烈预先存在问题的用户来说.
- 个性化信息传递增强了问题知识,但并没有在所有情况下实质性地改变政治态度.
结论:
- 基于LLM的VAA可以增强政治知识,特别是在与用户相关的特定问题上.
- 基于LLM的政治工具对下游行为 (如投票) 的影响是微妙的,并且可能受到现有的意识形态承诺的限制.
- 未来的研究应该探索扩大数字公民学习工具的覆盖范围和影响的策略.
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