UniversalRAG: Retrieval-Augmented Generation over Multiple Corpora with Diverse Modalities and Granularities

• URL: http://arxiv.org/abs/2504.20734v1
• Date: Tue, 29 Apr 2025 13:18:58 GMT
• Title: UniversalRAG: Retrieval-Augmented Generation over Multiple Corpora with Diverse Modalities and Granularities
• Authors: Woongyeong Yeo, Kangsan Kim, Soyeong Jeong, Jinheon Baek, Sung Ju Hwang,
• Abstract summary: UniversalRAG is a novel RAG framework designed to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities.<n>We propose a modality-aware routing mechanism that dynamically identifies the most appropriate modality-specific corpus and performs targeted retrieval within it.<n>We validate UniversalRAG on 8 benchmarks spanning multiple modalities, showing its superiority over modality-specific and unified baselines.
• Score: 53.76854299076118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. However, most existing RAG approaches are limited to a text-only corpus, and while recent efforts have extended RAG to other modalities such as images and videos, they typically operate over a single modality-specific corpus. In contrast, real-world queries vary widely in the type of knowledge they require, which a single type of knowledge source cannot address. To address this, we introduce UniversalRAG, a novel RAG framework designed to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities. Specifically, motivated by the observation that forcing all modalities into a unified representation space derived from a single combined corpus causes a modality gap, where the retrieval tends to favor items from the same modality as the query, we propose a modality-aware routing mechanism that dynamically identifies the most appropriate modality-specific corpus and performs targeted retrieval within it. Also, beyond modality, we organize each modality into multiple granularity levels, enabling fine-tuned retrieval tailored to the complexity and scope of the query. We validate UniversalRAG on 8 benchmarks spanning multiple modalities, showing its superiority over modality-specific and unified baselines.

Related papers

• HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation [11.53083922927901]
  HM-RAG is a novel Hierarchical Multi-agent Multimodal RAG framework.<n>It pioneers collaborative intelligence for dynamic knowledge synthesis across structured, unstructured, and graph-based data.
  arXiv  Detail & Related papers  (2025-04-13T06:55:33Z)
• GENIUS: A Generative Framework for Universal Multimodal Search [26.494338650656594]
  This paper proposes GENIUS, a universal generative retrieval framework supporting diverse tasks across multiple modalities and domains.<n>At its core, GENIUS introduces modality-decoupled semantic quantization, transforming multimodal data into discrete IDs encoding both modality and semantics.<n>To enhance generalization, we propose a query augmentation that interpolates between a query and its target, allowing GENIUS to adapt to varied query forms.
  arXiv  Detail & Related papers  (2025-03-25T17:32:31Z)
• Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning [51.54046200512198]
  Retrieval-augmented generation (RAG) is extensively utilized to incorporate external, current knowledge into large language models.<n>A standard RAG pipeline may comprise several components, such as query rewriting, document retrieval, document filtering, and answer generation.<n>To overcome these challenges, we propose treating the RAG pipeline as a multi-agent cooperative task, with each component regarded as an RL agent.
  arXiv  Detail & Related papers  (2025-01-25T14:24:50Z)
• Re-ranking the Context for Multimodal Retrieval Augmented Generation [28.63893944806149]
  Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge to generate a response within a context. RAG systems face unique challenges: (i) the retrieval process may select irrelevant entries to user query (e.g., images, documents), and (ii) vision-language models or multi-modal language models like GPT-4o may hallucinate when processing these entries to generate RAG output. We show that by using a more advanced relevancy measure, one can enhance the retrieval process by selecting more relevant pieces from the knowledge-base and eliminate
  arXiv  Detail & Related papers  (2025-01-08T18:58:22Z)
• Towards Modality Generalization: A Benchmark and Prospective Analysis [56.84045461854789]
  This paper introduces Modality Generalization (MG), which focuses on enabling models to generalize to unseen modalities. We propose a comprehensive benchmark featuring multi-modal algorithms and adapt existing methods that focus on generalization. Our work provides a foundation for advancing robust and adaptable multi-modal models, enabling them to handle unseen modalities in realistic scenarios.
  arXiv  Detail & Related papers  (2024-12-24T08:38:35Z)
• CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation [68.81271028921647]
  We introduce CORAL, a benchmark designed to assess RAG systems in realistic multi-turn conversational settings. CORAL includes diverse information-seeking conversations automatically derived from Wikipedia. It supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling.
  arXiv  Detail & Related papers  (2024-10-30T15:06:32Z)
• Retrieval Meets Reasoning: Even High-school Textbook Knowledge Benefits Multimodal Reasoning [49.3242278912771]
  We introduce a novel multimodal RAG framework named RMR (Retrieval Meets Reasoning) The RMR framework employs a bi-modal retrieval module to identify the most relevant question-answer pairs. It significantly boosts the performance of various vision-language models across a spectrum of benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-31T14:23:49Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)

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.