UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG
Authors: name: Xiangyu Peng, name: Cab Qin, name: Zeyuan Chen, name: Ran Xu, name: Caiming Xiong, name: Chien-Sheng Wu
Organization: Salesforce AI Research

Multimodal retrieval-augmented generation (MM-RAG) is a key approach for
applying large language models (LLMs) and agents to real-world knowledge bases,
yet current evaluations are fragmented, focusing on either text or images in
isolation or on simplified multimodal setups that fail to capture
document-centric multimodal use cases. In this paper, we introduce
UniDoc-Bench, the first large-scale, realistic benchmark for MM-RAG built from
70k real-world PDF pages across eight domains. Our pipeline extracts and links
evidence from text, tables, and figures, then generates 1,600 multimodal QA
pairs spanning factual retrieval, comparison, summarization, and logical
reasoning queries. To ensure reliability, 20% of QA pairs are validated by
multiple annotators and expert adjudication. UniDoc-Bench supports
apples-to-apples comparison across four paradigms: (1) text-only, (2)
image-only, (3) multimodal text-image fusion, and (4) multimodal joint
retrieval -- under a unified protocol with standardized candidate pools,
prompts, and evaluation metrics. Our experiments show that multimodal
text-image fusion RAG systems consistently outperform both unimodal and jointly
multimodal embedding-based retrieval, indicating that neither text nor images
alone are sufficient and that current multimodal embeddings remain inadequate.
Beyond benchmarking, our analysis reveals when and how visual context
complements textual evidence, uncovers systematic failure modes, and offers
actionable guidance for developing more robust MM-RAG pipelines.