Dynamic Large Language Models on Blockchains

Abstract: Training and deploying the LLMs requires a large mount of computational resource because the LLMs contain billions of parameters and the text has thousands of tokens. Another problem is that the LLMs are static. They are fixed after the training process. To tackle these issues, in this paper, we propose to train and deploy the dynamic LLM 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 LLMs can continuously learn from the user input after the training process. Our method provides a new way to develop the LLMs and also sheds a light on the next generation artificial intelligence systems.