基于大型语言模型的设计参与式预算规则
Large Language Models for Designing Participatory Budgeting Rules
AI总结：
本文提出LLMRule框架，利用大型语言模型自动化设计参与式预算规则，提升预算分配的效用同时保持公平性。
摘要：
参与式预算（PB）是一种民主范式，用于根据居民的偏好决定公共项目的资金分配，已在世界各地众多城市中被采用。PB的核心是设计规则，即在某些预算约束下，为一组项目返回可行的预算分配。基于代理偏好优化实用性和公平性目标的PB规则设计曾因需要大量的领域知识和两者之间的已知权衡而具有挑战性。最近，大型语言模型（LLMs）已被越来越多地用于自动化算法设计。鉴于PB规则与经典背包问题算法的相似性，本文介绍了一个新的框架，称为LLMRule，通过将LLMs纳入进化搜索过程来解决现有工作的局限性，以自动化设计PB规则。我们的实验结果，在美国、加拿大、波兰和荷兰获得的超过600个真实世界PB实例上进行评估，显示LLM生成的规则在总体效用方面通常优于现有手工规则，同时仍保持相似的公平程度。
英文摘要：
Participatory budgeting (PB) is a democratic paradigm for deciding the funding of public projects given the residents' preferences, which has been adopted in numerous cities across the world. The main focus of PB is designing rules, functions that return feasible budget allocations for a set of projects subject to some budget constraint. Designing PB rules that optimize both utility and fairness objectives based on agent preferences had been challenging due to the extensive domain knowledge required and the proven trade-off between the two notions. Recently, large language models (LLMs) have been increasingly employed for automated algorithmic design. Given the resemblance of PB rules to algorithms for classical knapsack problems, in this paper, we introduce a novel framework, named LLMRule, that addresses the limitations of existing works by incorporating LLMs into an evolutionary search procedure for automating the design of PB rules. Our experimental results, evaluated on more than 600 real-world PB instances obtained from the U.S., Canada, Poland, and the Netherlands with different representations of agent preferences, demonstrate that the LLM-generated rules generally outperform existing handcrafted rules in terms of overall utility while still maintaining a similar degree of fairness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
