用智能提问框架提升大模型对话中的信息获取效率
Ask Good Questions for Large Language Models
- 基于概念增强的项目反应理论，识别用户知识水平
- 生成精准引导问题，显著改善问答过程的信息检索
- 适合需要个性化引导的智能对话系统开发者
大语言模型在对话系统中的应用取得显著进展，但现有方法常因无法识别用户对相关概念的困惑而难以提供准确的话题引导。为此，我们提出Ask-Good-Question（AGQ）框架，引入改进的概念增强项目反应理论（CEIRT）模型，更精准地判断用户的知识水平。通过结合LLMs，该框架可直接根据提示文本生成引导性问题，大幅提高问答过程中的信息检索效率。与多种基线方法对比，本方法在用户信息获取体验上表现更优。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have significantly improved the performance of dialog systems, yet current approaches often fail to provide accurate guidance of topic due to their inability to discern user confusion in related concepts. To address this, we introduce the Ask-Good-Question (AGQ) framework, which features an improved Concept-Enhanced Item Response Theory (CEIRT) model to better identify users' knowledge levels. Our contributions include applying the CEIRT model along with LLMs to directly generate guiding questions based on the inspiring text, greatly improving information retrieval efficiency during the question & answer process. Through comparisons with other baseline methods, our approach outperforms by significantly enhencing the users' information retrieval experiences.
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