When interacting with Retrieval-Augmented Generation (RAG)-based conversational agents, the users must carefully craft their queries to be understood correctly. Yet, understanding the system's capabilities can be challenging for the users, leading to ambiguous questions that necessitate further clarification. This work aims to bridge the gap by developing a suggestion question generator. To generate suggestion questions, our approach involves utilizing dynamic context, which includes both dynamic few-shot examples and dynamically retrieved contexts. Through experiments, we show that the dynamic contexts approach can generate better suggestion questions as compared to other prompting approaches.

翻译：在与基于检索增强生成（RAG）的对话代理交互时，用户需精心设计查询以使其被正确理解。然而，理解系统能力对用户而言颇具挑战，导致产生需要进一步澄清的模糊问题。本研究旨在通过开发建议问题生成器来弥合这一差距。为生成建议问题，我们提出的方法涉及利用动态上下文，包括动态少样本示例和动态检索到的上下文。实验表明，与其他提示方法相比，动态上下文方法能生成更优的建议问题。