Fusion-Based Retrieval-Augmented Generation for Complex Question Answering with LLMs

This paper proposes a Retrieval-Augmented Generation (RAG) model that integrates structured and unstructured knowledge. The aim is to enhance the knowledge coverage and generation accuracy of large language models in complex question-answering tasks. The method introduces a dual-channel knowledge retrieval mechanism. One channel targets structured knowledge sources such as knowledge graphs and databases. The other focuses on unstructured textual resources such as documents and paragraphs. A unified knowledge fusion network integrates both types of heterogeneous information into a coherent generation context. The model performs multi-level modeling across key components. These include query representation generation, knowledge retrieval, representation alignment, and fusion expression construction. As a result, the generation stage produces text that is semantically rich and logically consistent. Under low-resource conditions, the method significantly improves the accuracy and linguistic quality of generated outputs. It also shows strong stability and generalization in cross-domain tasks. Systematic experiments were conducted on the proportion of structured knowledge, types of knowledge sources, and fusion strategies. The results demonstrate the effectiveness of the fusion architecture in enhancing knowledge representation in language models. This study provides a methodological foundation and empirical support for building controllable and trustworthy knowledge-enhanced natural language generation systems.