用采样算法从海量文本中选出各方都能接受的共识语句
Finding Common Ground in a Sea of Alternatives
- 基于社会选择理论设计采样算法，确保结果在无限选项中找共识
- 仅需少量投票者查询即可高概率找到符合比例否决核心的语句
- 适合需要生成跨群体可接受内容的AI系统，如公共政策建议
我们研究如何从多样化人群偏好中选出能达成共识的陈述。生成式AI能访问近乎无限的陈述集合，但现有系统如Habermas机器依赖投票规则决定选哪个，而该规则是否真正代表共识尚不明确。本文提出一种基于社会选择中比例否决核心（proportional veto core）的正式模型，用于处理无限选项场景。为在未知分布和大规模人群下提供保证，我们设计了一种高效的采样算法，仅通过有限查询就能以高概率返回近似比例否决核心中的陈述，并证明了查询次数的下界——任何算法都无法用更少查询实现同样保证。在合成文本偏好数据集上，验证了该算法的有效性，并对比了其他社会选择方法及基于LLM的方法在产出比例否决核心陈述上的可靠性。
原文摘要 · Abstract (English)
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.
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