基于RAG的对话系统中生成建议问题的动态上下文
Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systems
AI总结：
本研究针对RAG对话系统中用户查询模糊的问题，提出利用动态上下文（包括动态少样本示例和动态检索上下文）生成建议问题，实验证明该方法优于其他提示方法。
摘要：
在与基于检索增强生成（RAG）的对话代理交互时，用户必须精心构造其查询以确保被正确理解。然而，理解系统的能力对用户而言可能具有挑战性，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一个建议问题生成器来弥合这一差距。为生成建议问题，我们的方法利用动态上下文，包括动态少样本示例和动态检索的上下文。通过实验，我们表明与其他提示方法相比，动态上下文方法能够生成更好的建议问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
