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Large Language Models (LLMs), such as ChatGPT, have recently been applied tovarious NLP tasks due to its open-domain generation capabilities. However,there are two issues with applying LLMs to dialogue tasks. 1. During thedialogue process, users may have implicit intentions that might be overlookedby LLMs. Consequently, generated responses couldn't align with the user'sintentions. 2. It is unlikely for LLMs to encompass all fields comprehensively.In certain specific domains, their knowledge may be incomplete, and LLMs cannotupdate the latest knowledge in real-time. To tackle these issues, we propose aframework~\emph{using LLM to \textbf{E}nhance dialogue response generation byasking questions to \textbf{D}etect user's \textbf{I}mplicitin\textbf{T}entions} (\textbf{EDIT}). Firstly, EDIT generates open questionsrelated to the dialogue context as the potential user's intention; Then, EDITanswers those questions by interacting with LLMs and searching indomain-specific knowledge bases respectively, and use LLMs to choose the properanswers to questions as extra knowledge; Finally, EDIT enhances responsegeneration by explicitly integrating those extra knowledge. Besides, previousquestion generation works only focus on asking questions with answers incontext. In order to ask open questions, we construct a Context-Open-Question(COQ) dataset. On two task-oriented dialogue tasks (Wizard of Wikipedia andHoll-E), EDIT outperformed other LLMs.

A New Dialogue Response Generation Agent for Large Language Models by Asking Questions to Detect User's Intentions