Extending TriRAG for Advancing Retrieval-Augmented Generation Method with Triple-Based Knowledge Graphs for Improved Question Answering

While large language models exhibit broad capabilities, they often require supplementation for optimal performance on domain-specific tasks. To address this, we built TriRAG, an innovative enhancement to traditional Retrieval-Augmented Generation. This paper presents an extended version of our earlier TriRAG work. TriRAG integrates a structured knowledge graph composed of semantic triples derived from text, significantly improving the performance of LLMs on multiple-choice question-answering tasks. Our method dynamically converts relevant text into triples, embeds them into vectors, and retrieves the most useful triples for a given question by calculating vector similarities. By having the triple-based approach instead of the conventional text-based retrieval approach, TriRAG enables more precise and efficient information retrieval. This directly enhances the accuracy of LLM-generated responses in multiple-choice question-answering tasks. We evaluate TriRAG using the Textbook Question Answering dataset, demonstrating consistent improvements over traditional RAG methods across leading LLMs, including Gemma, Llama, and ChatGPT variants. Experimental results and ablation studies confirm that our triple-based system enhances both retrieval accuracy and processing efficiency, leading to better overall model performance.