2024/07/16 by Garima Agrawal, Agrawal, Garima, Tharindu Kumarage +5 · 6 citations
Computer Science · #AI in Service Interactions #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Intelligent Tutoring Systems and Adaptive Learning #Speech and dialogue systems
paper · pdf · doi:10.48550/arxiv.2407.12216
openalex publication_date 2024/07/16 · openalex created_date 2024/10/12 · openalex updated_date 2026/07/28
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.