Paper Title: ChainLM: Empowering the Future of Large Language Models through Blockchain-Powered Decentralization
Conference Name: JCCI
Abstract: The rapid growth of Large Language Models
(LLMs) has revolutionized various fields, from natural language
processing to AI-driven applications. However, the centralized
nature of their development and deployment presents significant
challenges in terms of accessibility, security, and control. This
paper introduces ChainLM, a novel framework that leverages
blockchain technology to decentralize access, thereby democratiz-
ing their use. By utilizing blockchain’s inherent features of trans-
parency, immutability, and decentralized consensus, ChainLM
ensures that these models are not only accessible to a broader
audience but also more secure, scalable, and fair. The frame-
work enables open collaboration, incentivizes contributions from
diverse stakeholders, and fosters trust by providing a transparent
mechanism for model training and validation. Additionally,
ChainLM addresses concerns regarding data privacy and model
biases through decentralized data storage and federated learning
techniques. This paper explores the technical architecture of
ChainLM, its potential applications, and the broader implications
of blockchain-powered decentralization in the AI landscape,
paving the way for more equitable and trustworthy AI systems.