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

翻译：大型语言模型（如ChatGPT）凭借其开放域生成能力，近期已被应用于各类自然语言处理任务。然而，将这类模型应用于对话任务时存在两个问题：（1）在对话过程中，用户可能隐含的意图易被LLM忽略，导致生成的回复无法契合用户意图；（2）LLM难以全面覆盖所有领域，在特定专业领域内其知识储备可能存在缺失，且无法实时更新最新信息。针对这些问题，我们提出一种框架——*利用LLM通过**提问**检测用户隐式**意图**以增强对话回复生成*（EDIT）。首先，EDIT生成与对话上下文相关的开放性问题作为潜在用户意图；其次，通过分别与LLM交互及在领域知识库中检索来回答问题，并利用LLM选取合适答案作为额外知识；最后，通过显式融合这些额外知识增强回复生成。此外，既往问题生成工作仅聚焦于生成上下文已知答案的问题。为生成开放性问题，我们构建了上下文-开放问题（COQ）数据集。在Wizard of Wikipedia和Holl-E两项任务导向型对话任务中，EDIT均优于其他LLM模型。