用知识图谱和大模型生成更深入的追问问题
From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM
- 三阶段流程：识别主题、在线构建知识图谱、融合大模型生成
- 生成的问题信息量更高，更接近人类提问水平
- 适合需要深度对话的智能客服、教育系统等场景
在对话系统中，基于上下文动态生成追问问题有助于用户探索信息并提升体验。人类常能提出涉及常识与高阶认知的提问，而现有方法生成的问题多为浅层上下文相关，缺乏启发性，与人类水平差距显著。本文提出一种三阶段外部知识增强的追问问题生成方法：通过识别上下文主题、在线构建知识图谱（KG），再结合大语言模型生成最终问题。该方法引入外部常识知识并进行知识融合，生成更具信息量和探索性的追问问题。实验表明，相比基线模型，该方法生成的问题更具信息量且更贴近人类提问水平，同时保持上下文相关性。
原文摘要 · Abstract (English)
In a conversational system, dynamically generating follow-up questions based on context can help users explore information and provide a better user experience. Humans are usually able to ask questions that involve some general life knowledge and demonstrate higher order cognitive skills. However, the questions generated by existing methods are often limited to shallow contextual questions that are uninspiring and have a large gap to the human level. In this paper, we propose a three-stage external knowledge-enhanced follow-up question generation method, which generates questions by identifying contextual topics, constructing a knowledge graph (KG) online, and finally combining these with a large language model to generate the final question. The model generates information-rich and exploratory follow-up questions by introducing external common sense knowledge and performing a knowledge fusion operation. Experiments show that compared to baseline models, our method generates questions that are more informative and closer to human questioning levels while maintaining contextual relevance.
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