The centralization of Large Language Models (LLMs) development has created significant barriers to AI advancement, limiting the democratization of these powerful technologies. This centralization, coupled with the scarcity of high-quality training data and mounting complexity of maintaining comprehensive expertise across rapidly expanding knowledge domains, poses critical challenges to the continued growth of LLMs. While solutions like Retrieval-Augmented Generation (RAG) offer potential remedies, maintaining up-to-date expert knowledge across diverse domains remains a significant challenge, particularly given the exponential growth of specialized information. This paper introduces LLMs Networks (LLM-Net), a blockchain-based framework that democratizes LLMs-as-a-Service through a decentralized network of specialized LLM providers. By leveraging collective computational resources and distributed domain expertise, LLM-Net incorporates fine-tuned expert models for various specific domains, ensuring sustained knowledge growth while maintaining service quality through collaborative prompting mechanisms. The framework's robust design includes blockchain technology for transparent transaction and performance validation, establishing an immutable record of service delivery. Our simulation, built on top of state-of-the-art LLMs such as Claude 3.5 Sonnet, Llama 3.1, Grok-2, and GPT-4o, validates the effectiveness of the reputation-based mechanism in maintaining service quality by selecting high-performing respondents (LLM providers). Thereby it demonstrates the potential of LLM-Net to sustain AI advancement through the integration of decentralized expertise and blockchain-based accountability.

翻译：大型语言模型（LLM）发展的中心化已成为人工智能进步的重大障碍，限制了这些强大技术的民主化进程。这种中心化，加之高质量训练数据的稀缺性，以及在快速扩张的知识领域中维持全面专业知识的复杂性日益增加，对LLM的持续发展构成了严峻挑战。尽管检索增强生成（RAG）等解决方案提供了潜在的补救措施，但在不同领域中维护最新的专家知识仍然是一项重大挑战，尤其是在专业信息呈指数级增长的背景下。本文介绍了LLM网络（LLM-Net），这是一个基于区块链的框架，它通过一个由专业化LLM提供商组成的去中心化网络，实现了LLM即服务的民主化。通过利用集体计算资源和分布式领域专业知识，LLM-Net整合了针对各种特定领域进行微调的专家模型，通过协作提示机制确保知识的持续增长，同时维持服务质量。该框架的稳健设计包含用于透明交易和性能验证的区块链技术，建立了服务交付的不可篡改记录。我们在Claude 3.5 Sonnet、Llama 3.1、Grok-2和GPT-4o等先进LLM基础上进行的模拟，验证了基于信誉的机制通过选择高性能响应者（LLM提供商）来维持服务质量的有效性。从而证明了LLM-Net通过整合去中心化专业知识和基于区块链的问责制来持续推动人工智能进步的潜力。