Dynamic Large Language Models on Blockchains
Yuanhao Gong
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.