Large language models (LLMs) are poised to revolutionize the domain of online fashion retail, enhancing customer experience and discovery of fashion online. LLM-powered conversational agents introduce a new way of discovery by directly interacting with customers, enabling them to express in their own ways, refine their needs, obtain fashion and shopping advice that is relevant to their taste and intent. For many tasks in e-commerce, such as finding a specific product, conversational agents need to convert their interactions with a customer to a specific call to different backend systems, e.g., a search system to showcase a relevant set of products. Therefore, evaluating the capabilities of LLMs to perform those tasks related to calling other services is vital. However, those evaluations are generally complex, due to the lack of relevant and high quality datasets, and do not align seamlessly with business needs, amongst others. To this end, we created a multilingual evaluation dataset of 4k conversations between customers and a fashion assistant in a large e-commerce fashion platform to measure the capabilities of LLMs to serve as an assistant between customers and a backend engine. We evaluate a range of models, showcasing how our dataset scales to business needs and facilitates iterative development of tools.

该研究解决了大语言模型（LLMs）在电子商务中评估其协助功能的不足，特别是在时尚领域的在线零售。通过创建一个包含4000条多语言对话的评估数据集，研究展示了LLMs如何有效地在客户与后台引擎之间进行互动，支持个性化的购物建议。该工作为时尚对话代理的实际应用提供了重要的数据支持，促进了相关工具的迭代开发。

我在希腊酒馆参加派对应穿什么？时尚领域对话代理的评估