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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. AI in Business
Volume 7 - 2024 | doi: 10.3389/frai.2024.1460217
This article is part of the Research Topic Large Language Models in Work and Business View all 4 articles

Towards Enhanced Creativity in Fashion: Integrating Generative Models with Hybrid Intelligence

Provisionally accepted
Alexander Ryjov Alexander Ryjov 1*Andrey Golub Andrey Golub 2Alina Egorova Alina Egorova 1
  • 1 MSU, Moscow, Russia
  • 2 ELSE Corp., Milano, Italy

The final, formatted version of the article will be published soon.

    This study offers several important insights into the role and potential use of large language models (LLMs) and generative intelligence in the fashion industry. The implementation of these technologies can significantly transform traditional approaches to design, production, and retail, providing new avenues for innovation, product personalization, and enhanced customer interaction. However, the study also identifies several limitations and challenges associated with the application of LLMs in the fashion industry, including the need for further model improvement to better understand spatial parameters and design details. One of the key areas for future research and development is the integration of LLMs into fashion industry workflows to maximize their potential and overcome existing limitations. This includes developing new approaches for iterative design enhancement based on hybrid intelligence, as well as creating more effective tools for personalization and customer interaction.

    Keywords: linguistic models (LLMs), generative intelligence (GI), Fashion industry, Hybrid intelligence, co-design

    Received: 05 Jul 2024; Accepted: 26 Aug 2024.

    Copyright: © 2024 Ryjov, Golub and Egorova. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Alexander Ryjov, MSU, Moscow, Russia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.