From BM25 to Corrective RAG: Benchmarking Retrieval Strategies for Text-and-Table Documents
Retrieval-Augmented Generation (RAG) systems critically depend on retrieval quality, yet no systematic comparison of modern retrieval methods exists for heterogeneous documents containing both text and tabular data. We benchmark ten retrieval strategies spanning sparse, dense, hybrid fusion, cross-encoder reranking, query expansion, index augmentation, and adaptive retrieval on a challenging financial QA benchmark of 23,088 queries over 7,318 documents with mixed text-and-table content. We evaluate retrieval quality via Recall@k, MRR, and nDCG, and end-to-end generation quality via Number Match, with paired bootstrap significance testing. Our results show that (1) a two-stage pipeline com...
| Authors | Meftun Akarsu, Recep Kaan Karaman, Christopher Mierbach |
|---|---|
| Primary category | cs.IR |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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