Large Language Models (LLMs) are proficient at generating coherent and contextually relevant text but face challenges when addressing knowledge-intensive queries in domain-specific and factual question-answering tasks. Retrieval-augmented generation (RAG) systems mitigate this by incorporating external knowledge sources, such as structured knowledge graphs (KGs). However, LLMs often struggle to produce accurate answers despite access to KG-extracted information containing necessary facts. Our study investigates this dilemma by analyzing error patterns in existing KG-based RAG methods and identifying eight critical failure points. We observed that these errors predominantly occur due to insufficient focus on discerning the question's intent and adequately gathering relevant context from the knowledge graph facts. Drawing on this analysis, we propose the Mindful-RAG approach, a framework designed for intent-based and contextually aligned knowledge retrieval. This method explicitly targets the identified failures and offers improvements in the correctness and relevance of responses provided by LLMs, representing a significant step forward from existing methods.

翻译：大型语言模型（LLM）擅长生成连贯且上下文相关的文本，但在处理特定领域和事实性问答任务中的知识密集型查询时面临挑战。检索增强生成（RAG）系统通过整合外部知识源（如结构化知识图谱）来缓解这一问题。然而，即使能够访问包含必要事实的KG提取信息，LLM仍常常难以生成准确的答案。本研究通过分析现有基于KG的RAG方法中的错误模式，并识别出八个关键失效点，来探究这一困境。我们观察到，这些错误主要源于对问题意图的辨别不足以及对知识图谱事实中相关上下文的充分收集不足。基于此分析，我们提出了Mindful-RAG方法，这是一个为基于意图和上下文对齐的知识检索而设计的框架。该方法明确针对已识别的失效点，并在LLM所提供答案的正确性和相关性方面提供了改进，代表了相对于现有方法的重要进步。