Digital assistants have become ubiquitous in e-commerce applications,
following the recent advancements in Information Retrieval (IR), Natural
Language Processing (NLP) and Generative Artificial Intelligence (AI). However,
customers are often unsure or unaware of how to effectively converse with these
assistants to meet their shopping needs. In this work, we emphasize the
importance of providing customers a fast, easy to use, and natural way to
interact with conversational shopping assistants. We propose a framework that
employs Large Language Models (LLMs) to automatically generate contextual,
useful, answerable, fluent and diverse questions about products, via in-context
learning and supervised fine-tuning. Recommending these questions to customers
as helpful suggestions or hints to both start and continue a conversation can
result in a smoother and faster shopping experience with reduced conversation
overhead and friction. We perform extensive offline evaluations, and discuss in
detail about potential customer impact, and the type, length and latency of our
generated product questions if incorporated into a real-world shopping
assistant.

通过使用大型语言模型（LLMs），我们提出了一个框架，可以自动生成与产品有关的上下文、有用、可回答、流畅且多样化的问题，并建议这些问题给客户作为帮助和提示，从而实现更顺畅、更快速、减少对话开销和摩擦的购物体验。