Training and deploying the large language models requires a large mount of computational resource because the language models contain billions of parameters and the text has thousands of tokens. Another problem is that the large language models are static. They are fixed after the training process. To tackle these issues, in this paper, we propose to train and deploy the dynamic large language model on blockchains, which have high computation performance and are distributed across a network of computers. A blockchain is a secure, decentralized, and transparent system that allows for the creation of a tamper-proof ledger for transactions without the need for intermediaries. The dynamic large language models can continuously learn from the user input after the training process. Our method provides a new way to develop the large language models and also sheds a light on the next generation artificial intelligence systems.

翻译：训练和部署大语言模型需要大量的计算资源，因为这些模型包含数十亿个参数，且文本序列包含数千个令牌。另一个问题是，大语言模型是静态的，其在训练过程结束后便固定不变。为解决这些问题，本文提出在区块链上训练和部署动态大语言模型。区块链具有高计算性能，且分布于由计算机组成的网络中。区块链是一种安全、去中心化且透明的系统，能够在无需中介的情况下创建防篡改的交易账本。动态大语言模型可在训练过程结束后持续从用户输入中学习。我们的方法为大语言模型的开发提供了新途径，也为下一代人工智能系统指明了方向。