Retrieval or Representation? Reassessing Benchmark Gaps in Multilingual and Visually Rich RAG
Retrieval-augmented generation (RAG) is a common way to ground language models in external documents and up-to-date information. Classical retrieval systems relied on lexical methods such as BM25, which rank documents by term overlap with corpus-level weighting. End-to-end multimodal retrievers trained on large query-document datasets claim substantial improvements over these approaches, especially for multilingual documents with complex visual layouts. We demonstrate that better document representation is the primary driver of benchmark improvements. By systematically varying transcription and preprocessing methods while holding the retrieval mechanism fixed, we demonstrate that BM25 can...
| Authors | Martin Asenov, Kenza Benkirane, Dan Goldwater et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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