mlsanityData to 14 Oct 2026

Viz: A QLoRA-based Copyright Marketplace for Legally Compliant Generative AI

arXiv 2401.00503 ↗First posted 31 Dec 2023cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 19 references that are in the map. First posted on arXiv 31 Dec 2023.

Builds on

3 of 19 references.

References · 3

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254

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Citing papers per year: 2025: 1.

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