大型语言模型 (LLM) 作为增强民主的代理人
Jairo F Gudiño1, Umberto Grandi2, César Hidalgo1,3
1Center for Collective Learning, University of Toulouse & Corvinus University of Budapest , Toulouse, France.
概括
大型语言模型 (LLM) 可以比传统方法更准确地预测个人和总体的公民政策偏好. 这种增强民主方法增强了公众论数据,超越党派界限,以获得更好的治理见解.
科学领域:
- 计算社会科学 计算社会科学
- 政治科学 政治科学是指政治学.
- 人工智能的人工智能
背景情况:
- 衡量论的传统方法可能无法捕捉微妙的政策偏好.
- 了解公民与政府计划的协调对于民主治理至关重要.
研究的目的:
- 通过使用大型语言模型 (LLM) 探索增强型民主系统,以增强有关公民政策偏好的数据.
- 评估LLM在预测基于政府计划的个人和总体政治选择方面的准确性.
- 调查LLM增强数据是否可以捕捉超越党派关系的政策偏好.
主要方法:
- 微调现成的大型语言模型 (LLM) 关于与巴西总统选举政策相关的公民偏好.
- 采用火车测试交叉验证设置来评估个人和总体层面的LLM预测准确性.
- 将LLM预测与"捆绑规则"和非增强的概率样本进行比较.
主要成果:
- 与"捆绑规则"相比,LLM在预测样本之外的个人偏好方面表现出更高的准确性.
- 与非增强样本相比,LLM增强的概率样本提供了对总人口偏好的更准确的估计.
- 通过LLM增强的数据成功地捕获了超越简单党派对齐的政策偏好.
结论:
- 用LLM增强的数据增强是捕获细微的公民政策偏好的一种有希望的方法.
- 这种方法提供了更准确的公众论表现,可能改善数字治理和参与式城市倡议.
- 该研究强调了人工智能的潜力,通过更好地了解公民与政策提案的协调来加强民主进程.
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