在众多替代品中寻找共同点
Finding Common Ground in a Sea of Alternatives
AI总结：
本文研究在多样偏好下选择能找到共同点的声明问题，提出基于比例否决核心的社会选择模型，并设计高效采样算法以高概率返回近似核心声明，同时证明了查询次数的下界。
摘要：
我们研究了在多样偏好下选择能找到共同点的声明的问题。生成式AI因其能访问几乎无限的声明集而特别适合此任务，但像哈贝马斯机器这样的AI系统将生成声明的选择交给投票规则。然而，这一规则如何找到共同点尚不明确。本文提出了一种基于社会选择中比例否决核心的正式模型，用于无限替代设置。为了在这些无限多替代品和大人口下提供保障，我们希望仅通过查询未知替代品和选民分布来满足比例否决核心的概念。我们设计了一个高效的基于采样的算法，以高概率返回核心中的替代品，并证明了匹配的下界，显示没有算法能用更少的查询做到这一点。在合成文本偏好的数据集上，我们确认了我们基于采样的算法的有效性，并比较了其他社会选择方法以及基于LLM的方法在产生核心中的声明的可靠性。
英文摘要：
We study the problem of selecting a statement that finds common ground across diverse population preferences. Generative AI is uniquely suited for this task because it can access a practically infinite set of statements, but AI systems like the Habermas machine leave the choice of generated statement to a voting rule. What it means for this rule to find common ground, however, is not well-defined. In this work, we propose a formal model for finding common ground in the infinite alternative setting based on the proportional veto core from social choice. To provide guarantees relative to these infinitely many alternatives and a large population, we wish to satisfy a notion of proportional veto core using only query access to the unknown distribution of alternatives and voters. We design an efficient sampling-based algorithm that returns an alternative in the (approximate) proportional veto core with high probability and prove matching lower bounds, which show that no algorithm can do the same using fewer queries. On a synthetic dataset of preferences over text, we confirm the effectiveness of our sampling-based algorithm and compare other social choice methods as well as LLM-based methods in terms of how reliably they produce statements in the proportional veto core.
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。
