Design and Development of Large Language Model Applied to Fashion Analysis

Abstract

Consumer preferences and intense market competition are characterized by dynamic due to the rapidly evolving fashion industry. However, existing trend analysis tools do not fully exploit large datasets, resulting in a lack of precision and timeliness in trend prediction, and often fail to grasp the complex nuances specific to the fashion sector. To address this research gap, this study aims to develop a cutting-edge, AI-driven model for fashion trend analysis and prediction, leveraging the power of big data and advanced machine learning technologies. A mixed method of qualitative and quantitative methods was conducted in this study. To be specific, the latest fashion trend data from WGSN including terncolor trends, fabrics and materials, and market analysis were used to collect data. These data were undergone thorough cleaning and preprocessing using NLTK. After that, the LLAMA2-7B model was selected for pre-training, which was then fine-tuned using LORA technology. Finally,the performance evaluation of the model was rigorously evaluated using an independent test set. Metrics including accuracy, recall, and F1 score were computed to assess the model's effectiveness. In addition, a manual evaluation was conducted, focusing on the specific analytical requirements of the fashion domain to ensure the model's validity and applicability. Furthermore, a user-friendly interface was developed, enabling both technical and non-technical users to easily utilize the model for fashion trend analysis and prediction. This study not only enhances the accuracy and efficiency of trend analysis and prediction but also offers valuable market insights and decision-making support for fashion designers, brand managers, and retailers. This advancement marks a substantial step forward in data analysis and prediction methodologies within the fashion industry, paving the way for new perspectives and approaches in future trend analysis and market strategy formulation.

Keywords: Large Language Model, Fashion Trend Analysis, Consumer preferences

DOI: 10.54941/ahfe1004917