用大模型生成能代表民意的政策建议清单，兼顾精准与长度限制。
Generative Social Choice: The Next Generation
- 结合社会选择理论与大模型，通过查询生成代表性意见
- 在预算长度内实现近似最优的民意覆盖，理论可证
- 适合城市治理、产品反馈等需提炼民意的场景
某些民主流程的核心任务是生成一份简洁的声明清单，以比例代表用户意见的全谱。这一任务类似于委员会选举，但候选集由所有可能的长短不一的陈述构成，只能通过特定查询访问。结合社会选择与大语言模型，已有研究提出生成式社会选择框架。本文从两个根本层面扩展该框架：即使在近似最优查询和整体清单长度受限的情况下，仍提供理论保证。我们使用GPT-4o实现查询，在城市改进措施和药物评价数据集上展示了该方法的有效性，能够从非结构化用户意见中生成具有代表性的声明清单。
原文摘要 · Abstract (English)
A key task in certain democratic processes is to produce a concise slate of statements that proportionally represents the full spectrum of user opinions. This task is similar to committee elections, but unlike traditional settings, the candidate set comprises all possible statements of varying lengths, and so it can only be accessed through specific queries. Combining social choice and large language models, prior work has approached this challenge through a framework of generative social choice. We extend the framework in two fundamental ways, providing theoretical guarantees even in the face of approximately optimal queries and a budget limit on the overall length of the slate. Using GPT-4o to implement queries, we showcase our approach on datasets related to city improvement measures and drug reviews, demonstrating its effectiveness in generating representative slates from unstructured user opinions.
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