Fashion-GPT: Integrating LLMs with Fashion Retrieval System
Qianqian Chen, Tianyi Zhang, Maowen Nie, Zheng Wang, Shihao Xu, Wei Shi, Zhao Cao
Abstract
Customers on a fashion e-commerce platform although expressing their clothing preferences through combined imagery and textual information, they are limited to retrieve with single-round fixed inputs. At the same time, large language models (LLMs) have been gaining attention across various fields. ChatGPT is a remarkable example of an LLM, known for its user-friendly language interface, impressive conversational proficiency, and reasoning abilities. To this end, we propose Fashion-GPT, a system paradigm that integrates ChatGPT with a pool of AI models in the fashion domain to achieve a multi-round multi-modal search. Specifically, it enables the system to utilize the LLMs for understanding user queries, select retrieval models based on their function descriptions, execute each subtask with the selected fashion models, and leverage LLMs to summarize the response corresponding to the execution results.