用算法从大量提案中筛选出高票项目，提升民主预算效率。
Algorithmic Shortlisting in Participatory Budgeting
- 基于投票历史和项目特征构建隐私保护筛选模型
- 新方法让大模型表现媲美传统机器学习模型
- 适合需要处理海量提案的公共预算决策场景
参与式预算是一种民主创新，允许公民提出并投票决定公共投资计划。为帮助组织者应对大量提案，我们设计并测试了隐私保护的算法筛选方法。这些算法仅利用项目特征和匿名的历史投票数据，预测哪些项目有望获得资助。我们揭示了仅依赖大语言模型根据过往成功案例排序的朴素方法的局限性，并提出一种基于投票的流程，使当前最先进的大语言模型性能可与经典机器学习方法相当。研究结果表明，参与式预算中的用户偏好具有足够稳定性，使得算法筛选能有效近似初始项目初选。
原文摘要 · Abstract (English)
Participatory budgeting is a democratic innovation that allows citizens to propose and vote on public investment projects. To help organizers manage large volumes of submissions, we design and test privacy-preserving methods for algorithmic shortlisting. These algorithms predict which projects are likely to be funded using only project features and anonymous historical voting data. We demonstrate the limitations of a naive approach that uses a large language model to rank projects based on past success and propose a vote-based pipeline that enables state-of-the-art LLMs to perform on par with classical machine learning. Our findings indicate that user preferences in participatory budgeting are stable enough to allow algorithmic shortlisting to approximate an initial selection of projects effectively.
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