The Heart of the Matter: Copyright, AI Training, and LLMs is a comprehensive analysis authored by Daniel Gervais, Haralambos Marmanis, Noam Shemtov, and Catherine Zaller Rowland. This work delves into the intricate relationship between copyright law and the development of large language models (LLMs) in artificial intelligence.

Key Themes:

• Technical Foundations of LLMs: The authors provide an in-depth explanation of LLMs, covering aspects such as tokenization, word embeddings, and the various stages of model development. This technical insight is essential for understanding the subsequent legal discussions.

• Copyright Implications: The paper examines potential copyright infringement issues related to both the inputs (training data) and outputs (generated content) of LLMs. It highlights the complexities of using vast amounts of copyrighted material in AI training processes.

• Comparative Legal Analysis: A comparative study is presented, focusing on jurisdictions including the United States, European Union, United Kingdom, Japan, Singapore, and Switzerland. The authors scrutinize relevant copyright exceptions and limitations, such as fair use in the U.S. and text and data mining exceptions in the EU.

• Licensing Solutions: Given the legal uncertainties, the authors advocate for licensing as a practical solution. They propose a combination of direct and collective licensing models to facilitate the responsible use of copyrighted materials in AI systems.

This article offers valuable insights for legal scholars, policymakers, and industry professionals grappling with the copyright challenges posed by LLMs. It contributes to the ongoing dialogue on adapting copyright law to technological advancements while maintaining its fundamental purpose of incentivizing creativity and innovation.

For a more detailed exploration, the full article is available on SSRN: SSRN