Viz: A QLoRA-based Copyright Marketplace for Legally Compliant Generative AI
It addresses the problem of legally compliant and efficient AI model usage for content creators, AI developers, and end-users, though it appears incremental as it builds on existing QLoRA and marketplace concepts.
The paper tackles the challenges of computational efficiency, legal compliance, and economic sustainability in using and monetizing large language models by proposing the Viz system, which integrates QLoRA for fine-tuning within a marketplace framework.
This paper aims to introduce and analyze the Viz system in a comprehensive way, a novel system architecture that integrates Quantized Low-Rank Adapters (QLoRA) to fine-tune large language models (LLM) within a legally compliant and resource efficient marketplace. Viz represents a significant contribution to the field of artificial intelligence, particularly in addressing the challenges of computational efficiency, legal compliance, and economic sustainability in the utilization and monetization of LLMs. The paper delineates the scholarly discourse and developments that have informed the creation of Viz, focusing primarily on the advancements in LLM models, copyright issues in AI training (NYT case, 2023), and the evolution of model fine-tuning techniques, particularly low-rank adapters and quantized low-rank adapters, to create a sustainable and economically compliant framework for LLM utilization. The economic model it proposes benefits content creators, AI developers, and end-users, delineating a harmonious integration of technology, economy, and law, offering a comprehensive solution to the complex challenges of today's AI landscape.