Recent debates raised concerns that language models may favor certain viewpoints. But what if the solution is not to aim for a 'view from nowhere' but rather to leverage different viewpoints? We introduce Plurals, a system and Python library for pluralistic AI deliberation. Plurals consists of Agents (LLMs, optionally with personas) which deliberate within customizable Structures, with Moderators overseeing deliberation. Plurals is a generator of simulated social ensembles. Plurals integrates with government datasets to create nationally representative personas, includes deliberation templates inspired by democratic deliberation theory, and allows users to customize both information-sharing structures and deliberation behavior within Structures. Six case studies demonstrate fidelity to theoretical constructs and efficacy. Three randomized experiments show simulated focus groups produced output resonant with an online sample of the relevant audiences (chosen over zero-shot generation in 75% of trials). Plurals is both a paradigm and a concrete system for pluralistic AI. The Plurals library is available at https://github.com/josh-ashkinaze/plurals and will be continually updated.

翻译：近期争论引发了对语言模型可能偏向特定观点的担忧。但若解决方案并非追求"无立场视角"，而是利用不同观点呢？我们提出Plurals——一个用于多元人工智能审议的系统及Python库。Plurals由智能体（大语言模型，可选配人物角色）组成，这些智能体在可定制的结构中开展审议，并由监督员管控审议过程。Plurals是模拟社会集合的生成器。该系统整合政府数据集以创建具有全国代表性的人物角色，包含受民主审议理论启发的审议模板，并允许用户自定义信息共享结构和结构内的审议行为。六项案例研究验证了系统对理论构念的忠实度和实际效能。三项随机实验表明，模拟焦点小组生成的输出与相关受众在线样本产生共鸣（在75%的试验中优于零样本生成）。Plurals既是多元人工智能的范式，也是具体实现系统。Plurals库可通过https://github.com/josh-ashkinaze/plurals获取并将持续更新。