Polis is a platform that leverages machine intelligence to scale up deliberative processes. In this paper, we explore the opportunities and risks associated with applying Large Language Models (LLMs) towards challenges with facilitating, moderating and summarizing the results of Polis engagements. In particular, we demonstrate with pilot experiments using Anthropic's Claude that LLMs can indeed augment human intelligence to help more efficiently run Polis conversations. In particular, we find that summarization capabilities enable categorically new methods with immense promise to empower the public in collective meaning-making exercises. And notably, LLM context limitations have a significant impact on insight and quality of these results. However, these opportunities come with risks. We discuss some of these risks, as well as principles and techniques for characterizing and mitigating them, and the implications for other deliberative or political systems that may employ LLMs. Finally, we conclude with several open future research directions for augmenting tools like Polis with LLMs.

翻译：Polis是一个利用机器智能扩展协商过程的平台。本文探讨了将大型语言模型（LLMs）应用于Polis活动的促进、调解及结果总结所面临的机遇与风险。通过使用Anthropic的Claude进行试点实验，我们证明LLMs确实能够增强人类智能，助力更高效地运行Polis对话。特别地，我们发现LLM的总结能力能够催生出全新的方法，在集体意义构建实践中赋予公众巨大潜力。值得注意的是，LLM的上下文限制对成果的深度与质量具有显著影响。然而，这些机遇伴随着风险。我们讨论了部分风险，以及表征与缓解风险的原则和技术，并阐述了这对可能采用LLMs的其他协商或政治系统的启示。最后，我们提出了若干未来研究方向，以探索如何借助LLMs增强诸如Polis等工具。