MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A
MM-BizRAG is a novel multimodal retrieval-augmented generation approach for enterprise Q&A. It proactively extracts document structure via a structure-aware split and orientation-specific ingestion pipelines, enabling richer answers without finetuning. Experiments show up to 32% improvement over state-of-the-art baselines on enterprise and public benchmarks. The paper also introduces FastRAGEval, a cost-effective LLM judge metric. Accepted at ACL 2026 Industry Track.
[2606.04231] MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A
[Submitted on 2 Jun 2026]
Title:MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A
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Abstract:Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation. While efficient, this trend often neglects explicit handling of the rich, structured information in complex enterprise documents, instead depending on pre-trained embeddings or vision-language models to implicitly capture such structure. In this work, we take a more direct approach: MM-BizRAG proactively extracts and represents document structure via a document structure-aware split that dynamically routes documents through orientation-specific ingestion pipelines, applying explicit layout-aware parsing for vertically structured documents (e.g., reports) and holistic page-level representations for horizontally structured documents (e.g., slide decks). A unified LLM-driven artifact transformation pipeline with placeholder-based positional alignment preserves natural reading order, while inference-time multimodal assembly decouples retrieval representations from generation context, enabling richer, more grounded answers without any finetuning requirement. Through experiments on a large, heterogeneous enterprise dataset and two public benchmarks (SlideVQA and FinRAGBench-V), MM-BizRAG consistently outperforms state-of-the-art vision-centric baselines by up to 32% points, with especially strong gains on report-style layouts. Furthermore, we introduce FastRAGEval, a single-call LLM Judge metric for fine-grained generative recall that halves RAGChecker's cost while achieving stronger human alignment.
Comments: Accepted at ACL 2026 (Industry Track)
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.04231 [cs.CL]
(or arXiv:2606.04231v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.04231
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hanoz Bhathena [view email] [v1] Tue, 2 Jun 2026 21:31:47 UTC (934 KB)
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