LLM在与Polis进行可扩展审议的机会与风险

摘要

Polis是一个利用机器智能来扩展审慎过程的平台。在本文中，我们探讨了应用大型语言模型（LLMs）解决Polis参与过程中促进、管理和总结结果所面临的机遇和风险。特别是，我们通过使用Anthropic的Claude进行试点实验来展示LLMs确实可以增强人类智能，以更有效地运行Polis对话。我们发现，总结能力使得全新的方法具有巨大潜力，有助于赋予公众在集体意义塑造练习中更多权力。值得注意的是，LLM的上下文限制对这些结果的洞察力和质量有重要影响。然而，这些机遇伴随着风险。我们讨论了其中一些风险，以及表征和减轻这些风险的原则和技术，以及对可能采用LLMs的其他审慎或政治系统的影响。最后，我们总结了几个未来开放研究方向，以增强类似Polis的工具与LLMs的结合。

English

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.

LLM在与Polis进行可扩展审议的机会与风险

Opportunities and Risks of LLMs for Scalable Deliberation with Polis

摘要

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