mRAG: Elucidating the Design Space of Multi-modal Retrieval-Augmented Generation

2025-05-29

AI Summary

Large vision-language models (LVLMs) suffer from weak factual consistency and poor dynamic adaptability due to static pretraining, frequent hallucinations, and the absence of external knowledge verification mechanisms. Method: This work introduces the first systematic multimodal Retrieval-Augmented Generation (RAG) framework for LVLMs. It proposes (i) cross-modal retrieval alignment, (ii) a position-bias-corrected re-ranking mechanism, (iii) retrieval-evidence-conditioned generation, and (iv) a unified self-reflective agent for dynamic evidence selection and irrelevant context suppression—all without model fine-tuning. Contribution/Results: Evaluated on multiple multimodal question answering and reasoning benchmarks, the framework achieves an average 5% performance gain, significantly improving factual accuracy and real-time external knowledge utilization while preserving model parameter integrity.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: Language Grounding & Multi-modal NLPComputer Vision: Multi-modal Vision

Application Category

Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAGUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large Vision-Language Models (LVLMs) have made remarkable strides in multimodal tasks such as visual question answering, visual grounding, and complex reasoning. However, they remain limited by static training data, susceptibility to hallucinations, and inability to verify claims against up-to-date, external evidence, compromising their performance in dynamic real-world applications. Retrieval-Augmented Generation (RAG) offers a practical solution to mitigate these challenges by allowing the LVLMs to access large-scale knowledge databases via retrieval mechanisms, thereby grounding model outputs in factual, contextually relevant information. Here in this paper, we conduct the first systematic dissection of the multimodal RAG pipeline for LVLMs, explicitly investigating (1) the retrieval phase: on the modality configurations and retrieval strategies, (2) the re-ranking stage: on strategies to mitigate positional biases and improve the relevance of retrieved evidence, and (3) the generation phase: we further investigate how to best integrate retrieved candidates into the final generation process. Finally, we extend to explore a unified agentic framework that integrates re-ranking and generation through self-reflection, enabling LVLMs to select relevant evidence and suppress irrelevant context dynamically. Our full-stack exploration of RAG for LVLMs yields substantial insights, resulting in an average performance boost of 5% without any fine-tuning.

Problem

• Enhancing LVLMs with dynamic external knowledge access
• Optimizing multimodal retrieval and re-ranking strategies
• Improving generation accuracy via evidence integration

Innovation

• Multimodal RAG pipeline for LVLMs
• Dynamic evidence re-ranking and integration
• Agentic framework with self-reflection