UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG

• URL: http://arxiv.org/abs/2510.03663v2
• Date: Thu, 09 Oct 2025 05:30:23 GMT
• Title: UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG
• Authors: Xiangyu Peng, Can Qin, Zeyuan Chen, Ran Xu, Caiming Xiong, Chien-Sheng Wu,
• Abstract summary: Multimodal retrieval-augmented generation (MM-RAG) is a key approach for applying large language models to real-world knowledge bases.<n>UniDoc-Bench is the first large-scale, realistic benchmark for MM-RAG built from 70k real-world PDF pages.<n>Our experiments show that multimodal text-image fusion RAG systems consistently outperform both unimodal and jointly multimodal embedding-based retrieval.
• Score: 82.84014669683863
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents [37.98503734345155]
  We introduce MMDeepResearch-Bench (MMDR-Bench), a benchmark of 140 expert-crafted tasks across 21 domains.<n>Compared to prior setups, MMDR-Bench emphasizes report-style synthesis with explicit evidence use.<n>We propose a unified, interpretable evaluation pipeline: Formula-LLM Adaptive Evaluation (FLAE) for report quality, Trustworthy Retrieval-Aligned Citation Evaluation (TRACE) for citation-grounded evidence alignment, and Multimodal Support-Aligned Integrity Check (MOSAIC) for text-visual integrity.
  arXiv  Detail & Related papers  (2026-01-18T10:41:33Z)
• MM-BRIGHT: A Multi-Task Multimodal Benchmark for Reasoning-Intensive Retrieval [18.53521844184766]
  MM-BRIGHT is the first multimodal benchmark for reasoning-intensive retrieval.<n>Our dataset consists of 2,803 real-world queries spanning 29 diverse technical domains.
  arXiv  Detail & Related papers  (2026-01-14T15:31:54Z)
• Recurrence Meets Transformers for Universal Multimodal Retrieval [59.92546492752452]
  ReT-2 is a unified retrieval model that supports multimodal queries composed of both images and text.<n>We evaluate ReT-2 on the challenging M2KR and M-BEIR benchmarks across different retrieval configurations.<n>When integrated into retrieval-augmented generation pipelines, ReT-2 also improves downstream performance on Encyclopedic-VQA and InfoSeek datasets.
  arXiv  Detail & Related papers  (2025-09-10T18:00:29Z)
• CMRAG: Co-modality-based visual document retrieval and question answering [21.016544020685668]
  Co-Modality-based RAG (RAG) framework can leverage texts and images for more accurate retrieval and generation.<n>Our framework consistently outperforms single-modality-based RAG in multiple visual document question-answering (VDQA) benchmarks.
  arXiv  Detail & Related papers  (2025-09-02T09:17:57Z)
• Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation [47.714317480436215]
  PREMIR is a simple framework that leverages the broad knowledge of an MLLM to generate cross modal pre questions (preQs) before retrieval.<n> Experiments show that PREMIR achieves state of the art performance on out of distribution benchmarks, including closed domain and multilingual settings.
  arXiv  Detail & Related papers  (2025-08-23T16:14:41Z)
• MMESGBench: Pioneering Multimodal Understanding and Complex Reasoning Benchmark for ESG Tasks [56.350173737493215]
  Environmental, Social, and Governance (ESG) reports are essential for evaluating sustainability practices, ensuring regulatory compliance, and promoting financial transparency.<n>MMESGBench is a first-of-its-kind benchmark dataset to evaluate multimodal understanding and complex reasoning across structurally diverse and multi-source ESG documents.<n>MMESGBench comprises 933 validated QA pairs derived from 45 ESG documents, spanning across seven distinct document types and three major ESG source categories.
  arXiv  Detail & Related papers  (2025-07-25T03:58:07Z)
• Benchmarking Retrieval-Augmented Multimomal Generation for Document Question Answering [42.468210353582755]
  Document Visual Question Answering (DocVQA) faces dual challenges in processing lengthy multimodal documents.<n>Current document retrieval-augmented generation (DocRAG) methods remain limited by their text-centric approaches.<n>We introduce MMDocRAG, a comprehensive benchmark featuring 4,055 expert-annotated QA pairs with multi-page, cross-modal evidence chains.
  arXiv  Detail & Related papers  (2025-05-22T09:52:57Z)
• Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts [56.7225771305861]
  This paper introduces Multi-Modal Retrieval-Augmented Generation (M$2$RAG), a benchmark designed to evaluate the effectiveness of Multi-modal Large Language Models.<n>The benchmark comprises four tasks: image captioning, multi-modal question answering, multi-modal fact verification, and image reranking.<n>To enhance the context utilization capabilities of MLLMs, we also introduce Multi-Modal Retrieval-Augmented Instruction Tuning (MM-RAIT)
  arXiv  Detail & Related papers  (2025-02-24T16:25:25Z)
• VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation [100.06122876025063]
  This paper introduces VisDoMBench, the first comprehensive benchmark designed to evaluate QA systems in multi-document settings.<n>We propose VisDoMRAG, a novel multimodal Retrieval Augmented Generation (RAG) approach that simultaneously utilizes visual and textual RAG.
  arXiv  Detail & Related papers  (2024-12-14T06:24:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.