Recent developments in Large Language Models (LLMs) have significantly
expanded their applications across various domains. However, the effectiveness
of LLMs is often constrained when operating individually in complex
environments. This paper introduces a transformative approach by organizing
LLMs into community-based structures, aimed at enhancing their collective
intelligence and problem-solving capabilities. We investigate different
organizational models-hierarchical, flat, dynamic, and federated-each
presenting unique benefits and challenges for collaborative AI systems. Within
these structured communities, LLMs are designed to specialize in distinct
cognitive tasks, employ advanced interaction mechanisms such as direct
communication, voting systems, and market-based approaches, and dynamically
adjust their governance structures to meet changing demands. The implementation
of such communities holds substantial promise for improve problem-solving
capabilities in AI, prompting an in-depth examination of their ethical
considerations, management strategies, and scalability potential. This position
paper seeks to lay the groundwork for future research, advocating a paradigm
shift from isolated to synergistic operational frameworks in AI research and
application.

通过将大型语言模型（LLMs）组织成以社区为基础的结构，该论文介绍了一种变革性方法，旨在增强 LLMs 的集体智能和问题解决能力。本文研究了不同的组织模型 - 分层、扁平、动态和联邦，每种模型都具有协作人工智能系统的独特优势和挑战。在这些结构化社区中，LLMs 被设计为专门从事不同的认知任务，采用直接通信、投票系统和基于市场的方法等先进的交互机制，并动态调整其治理结构以满足不断变化的需求。这种社区的实施对于提高人工智能的问题解决能力具有重大潜力，因此有必要深入研究其伦理考虑、管理策略和可扩展性。本文旨在为未来研究奠定基础，主张在人工智能研究和应用中从孤立到协同的运行框架进行范式转变。

组织一种语言模型社会：结构和机制以促进集体智慧的提升

Organizing a Society of Language Models: Structures and Mechanisms for  Enhanced Collective Intelligence

This paper investigates the potential of AI models, particularly large
language models (LLMs), to support knowledge exploration and augment human
creativity during ideation. We present "Latent Lab" an interactive tool for
discovering connections among MIT Media Lab research projects, emphasizing
"exploration" over search. The work offers insights into collaborative AI
systems by addressing the challenges of organizing, searching, and synthesizing
content. In a user study, the tool's success was evaluated based on its ability
to introduce users to an unfamiliar knowledge base, ultimately setting the
groundwork for the ongoing advancement of human-AI knowledge exploration
systems.

本文研究人工智能模型（特别是大型语言模型）在知识探索和创意增强过程中的潜力。我们展示了一个名为 “潜在实验室” 的交互工具，用于发现麻省理工媒体实验室研究项目之间的联系，强调 “探索” 而非搜索。通过解决组织、搜索和综合内容的挑战，这项工作为协作式人工智能系统提供了深入见解。在一项用户研究中，根据该工具引入用户到一个陌生的知识库的能力来评估其成功，最终为人工智能知识探索系统的不断进步奠定了基础。