FUSE-RAG: A Unified Architecture for Retrieval-Augmented Generation
Large language models may suffer from insufficient context use and unsupported generation in question answering tasks that require external knowledge. This study compares the main strategies affecting retrieval and generation performance in retrieval-augmented generation systems within a common experimental setting. In addition to a standard dense retrieval baseline, we evaluate multi-query, hypothetical document, hypothetical question, sparse-dense hybrid retrieval, and chunk compression, and we also propose an integrated method (FUSERAG) that combines these components. Experiments are conducted on a dataset containing 223 documents, 7933 chunks, and 1488 question-answer instances. Results show that the proposed method achieves the highest Mean Reciprocal Rank, nDCG at 5, and recall at 5 at the retrieval level, while the sparse-dense hybrid approach yields the best generation results.