Mixture-of-RAG: Integrating Text and Tables with Large Language Models

• URL: http://arxiv.org/abs/2504.09554v2
• Date: Mon, 11 Aug 2025 18:03:50 GMT
• Title: Mixture-of-RAG: Integrating Text and Tables with Large Language Models
• Authors: Chi Zhang, Qiyang Chen, Mengqi Zhang,
• Abstract summary: Heterogeneous Document RAG requires joint retrieval and reasoning across textual and hierarchical data.<n>We propose MixRAG, a novel three-stage framework that preserves hierarchical structure and heterogeneous relationships.<n>Experiments show that MixRAG boosts top-1 retrieval by 46% over strong text-only, table-only, and naive-mixture baselines.
• Score: 5.038576104344948
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) achieve optimal utility when their responses are grounded in external knowledge sources. However, real-world documents, such as annual reports, scientific papers, and clinical guidelines, frequently combine extensive narrative content with complex, hierarchically structured tables. While existing retrieval-augmented generation (RAG) systems effectively integrate LLMs' generative capabilities with external retrieval-based information, their performance significantly deteriorates when processing such heterogeneous text-table hierarchies. To address this limitation, we formalize the task of Heterogeneous Document RAG, which requires joint retrieval and reasoning across textual and hierarchical tabular data. We propose MixRAG, a novel three-stage framework: (i) hierarchy row-and-column-level (H-RCL) representation that preserves hierarchical structure and heterogeneous relationships, (ii) an ensemble retriever with LLM-based reranking for evidence alignment, and (iii) multi-step reasoning decomposition via a RECAP prompt strategy. To bridge the gap in available data for this domain, we release a large-scale dataset, DocRAGLib, a 2k-document corpus paired with automatically aligned text-table summaries and gold document annotations. The comprehensive experimental results demonstrate that MixRAG boosts top-1 retrieval by 46% over strong text-only, table-only, and naive-mixture baselines, establishing new state-of-the-art performance for mixed-modality document grounding.

Related papers

• MoDora: Tree-Based Semi-Structured Document Analysis System [62.01015188258797]
  Semi-structured documents integrate diverse interleaved data elements arranged in various and often irregular layouts.<n>MoDora is an LLM-powered system for semi-structured document analysis.<n> Experiments show MoDora outperforms baselines by 5.97%-61.07% in accuracy.
  arXiv  Detail & Related papers  (2026-02-26T14:48:49Z)
• BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents [11.158307125677375]
  Retrieval-Augmented Generation (RAG) queries highly relevant information from external complex documents.<n>We introduce BookRAG, a novel RAG approach targeted for documents with a hierarchical structure.<n>BookRAG achieves state-of-the-art performance, significantly outperforming baselines in both retrieval recall and QA accuracy.
  arXiv  Detail & Related papers  (2025-12-03T03:40:49Z)
• MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns [80.05126590825121]
  MonkeyOCR v1.5 is a unified vision-language framework that enhances both layout understanding and content recognition.<n>To address complex table structures, we propose a visual consistency-based reinforcement learning scheme.<n>Two specialized modules, Image-Decoupled Table Parsing and Type-Guided Table Merging, are introduced to enable reliable parsing of tables.
  arXiv  Detail & Related papers  (2025-11-13T15:12:17Z)
• Towards Mixed-Modal Retrieval for Universal Retrieval-Augmented Generation [72.34977512403643]
  Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) by retrieving relevant documents from an external corpus.<n>Existing RAG systems primarily focus on unimodal text documents, and often fall short in real-world scenarios where both queries and documents may contain mixed modalities (such as text and images)<n>We propose Nyx, a unified mixed-modal to mixed-modal retriever tailored for Universal Retrieval-Augmented Generation scenarios.
  arXiv  Detail & Related papers  (2025-10-20T09:56:43Z)
• Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
  Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
  arXiv  Detail & Related papers  (2025-10-17T02:33:16Z)
• 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)
• HyST: LLM-Powered Hybrid Retrieval over Semi-Structured Tabular Data [0.4779196219827507]
  HyST (Hybrid retrieval over Semi-structured Tabular data) is a hybrid retrieval framework that combines structured filtering with semantic embedding search.<n>We show that HyST consistently outperforms tradtional baselines on a semi-structured benchmark.
  arXiv  Detail & Related papers  (2025-08-25T14:06:27Z)
• Benchmarking Multimodal Understanding and Complex Reasoning 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)
• Beyond Isolated Dots: Benchmarking Structured Table Construction as Deep Knowledge Extraction [80.88654868264645]
  Arranged and Organized Extraction Benchmark designed to evaluate ability of large language models to comprehend fragmented documents.<n>AOE includes 11 carefully crafted tasks across three diverse domains, requiring models to generate context-specific schema tailored to varied input queries.<n>Results show that even the most advanced models struggled significantly.
  arXiv  Detail & Related papers  (2025-07-22T06:37:51Z)
• TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning [3.1480184228320205]
  Retrieval-Augmented Generation (RAG) has demonstrated considerable effectiveness in open-domain question answering.<n>Existing RAG approaches exhibit critical limitations when applied to heterogeneous documents.<n>We propose TableRAG, a framework that unifies textual understanding and complex manipulations over tabular data.<n>We also develop HeteQA, a novel benchmark designed to evaluate the multi-hop heterogeneous reasoning capabilities.
  arXiv  Detail & Related papers  (2025-06-12T06:16:49Z)
• Large Language Models are Good Relational Learners [55.40941576497973]
  We introduce Rel-LLM, a novel architecture that utilizes a graph neural network (GNN)- based encoder to generate structured relational prompts for large language models (LLMs)<n>Unlike traditional text-based serialization approaches, our method preserves the inherent relational structure of databases while enabling LLMs to process and reason over complex entity relationships.
  arXiv  Detail & Related papers  (2025-06-06T04:07:55Z)
• Can LLMs Generate Tabular Summaries of Science Papers? Rethinking the Evaluation Protocol [83.90769864167301]
  Literature review tables are essential for summarizing and comparing collections of scientific papers. We explore the task of generating tables that best fulfill a user's informational needs given a collection of scientific papers. Our contributions focus on three key challenges encountered in real-world use: (i) User prompts are often under-specified; (ii) Retrieved candidate papers frequently contain irrelevant content; and (iii) Task evaluation should move beyond shallow text similarity techniques.
  arXiv  Detail & Related papers  (2025-04-14T14:52:28Z)
• Generative Retrieval for Book search [106.67655212825025]
  We propose an effective Generative retrieval framework for Book Search.<n>It features two main components: data augmentation and outline-oriented book encoding.<n>Experiments on a proprietary Baidu dataset demonstrate that GBS outperforms strong baselines.
  arXiv  Detail & Related papers  (2025-01-19T12:57:13Z)
• KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models [38.93603907879804]
  We introduce a novel Knowledge Graph-based RAG framework with a hierarchical knowledge retriever, termed KG-Retriever. The associative nature of graph structures is fully utilized to strengthen intra-document and inter-document connectivity. With the coarse-grained collaborative information from neighboring documents and concise information from the knowledge graph, KG-Retriever achieves marked improvements on five public QA datasets.
  arXiv  Detail & Related papers  (2024-12-07T05:49:14Z)
• ConTReGen: Context-driven Tree-structured Retrieval for Open-domain Long-form Text Generation [26.4086456393314]
  Long-form text generation requires coherent, comprehensive responses that address complex queries with both breadth and depth. Existing iterative retrieval-augmented generation approaches often struggle to delve deeply into each facet of complex queries. This paper introduces ConTReGen, a novel framework that employs a context-driven, tree-structured retrieval approach.
  arXiv  Detail & Related papers  (2024-10-20T21:17:05Z)
• DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering [4.364937306005719]
  RAG has recently demonstrated the performance of Large Language Models (LLMs) in the knowledge-intensive tasks such as Question-Answering (QA) We have found that even though there is low relevance between some critical documents and query, it is possible to retrieve the remaining documents by combining parts of the documents with the query. A two-stage retrieval framework called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) is proposed to improve document retrieval recall and the accuracy of answers.
  arXiv  Detail & Related papers  (2024-06-11T15:15:33Z)
• Multi-Head RAG: Solving Multi-Aspect Problems with LLMs [13.638439488923671]
  Retrieval Augmented Generation (RAG) enhances the abilities of Large Language Models (LLMs) Existing RAG solutions do not focus on queries that may require fetching multiple documents with substantially different contents. This paper introduces Multi-Head RAG (MRAG), a novel scheme designed to address this gap with a simple yet powerful idea.
  arXiv  Detail & Related papers  (2024-06-07T16:59:38Z)
• TACT: Advancing Complex Aggregative Reasoning with Information Extraction Tools [51.576974932743596]
  Large Language Models (LLMs) often do not perform well on queries that require the aggregation of information across texts. TACT contains challenging instructions that demand stitching information scattered across one or more texts. We construct this dataset by leveraging an existing dataset of texts and their associated tables. We demonstrate that all contemporary LLMs perform poorly on this dataset, achieving an accuracy below 38%.
  arXiv  Detail & Related papers  (2024-06-05T20:32:56Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models [0.0]
  The conventional use of the Retrieval-Augmented Generation architecture has proven effective for retrieving information from diverse documents. This research introduces an innovative approach to enhance the accuracy of complex table queries in RAG-based systems.
  arXiv  Detail & Related papers  (2024-01-04T16:16:14Z)
• Decomposing Complex Queries for Tip-of-the-tongue Retrieval [72.07449449115167]
  Complex queries describe content elements (e.g., book characters or events), information beyond the document text. This retrieval setting, called tip of the tongue (TOT), is especially challenging for models reliant on lexical and semantic overlap between query and document text. We introduce a simple yet effective framework for handling such complex queries by decomposing the query into individual clues, routing those as sub-queries to specialized retrievers, and ensembling the results.
  arXiv  Detail & Related papers  (2023-05-24T11:43:40Z)
• Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA [85.17249272519626]
  An optimized OpenQA Table-Text Retriever (OTTeR) is proposed. We conduct retrieval-centric mixed-modality synthetic pre-training. OTTeR substantially improves the performance of table-and-text retrieval on the OTT-QA dataset.
  arXiv  Detail & Related papers  (2022-10-11T07:04:39Z)

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.