Augmenting Question Answering with A Hybrid RAG Approach
This addresses the problem of poor retrieval in QA systems for users needing accurate information, though it appears incremental as it builds on existing RAG techniques.
The paper tackles the problem of incomplete or suboptimal answers in Question-Answering tasks by introducing Structured-Semantic RAG (SSRAG), a hybrid architecture that integrates query augmentation, agentic routing, and structured retrieval. The approach improves answer accuracy and informativeness, as demonstrated by consistent improvements over standard RAG implementations across three QA datasets and five LLMs.
Retrieval-Augmented Generation (RAG) has emerged as a powerful technique for enhancing the quality of responses in Question-Answering (QA) tasks. However, existing approaches often struggle with retrieving contextually relevant information, leading to incomplete or suboptimal answers. In this paper, we introduce Structured-Semantic RAG (SSRAG), a hybrid architecture that enhances QA quality by integrating query augmentation, agentic routing, and a structured retrieval mechanism combining vector and graph based techniques with context unification. By refining retrieval processes and improving contextual grounding, our approach improves both answer accuracy and informativeness. We conduct extensive evaluations on three popular QA datasets, TruthfulQA, SQuAD and WikiQA, across five Large Language Models (LLMs), demonstrating that our proposed approach consistently improves response quality over standard RAG implementations.