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Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation
Mohammad Mahdi Abootorabi, Amirhosein Zobeiri, Mahdi Dehghani, Mohammadali Mohammadkhani, Bardia Mohammadi, Omid Ghahroodi, Mahdieh Soleymani Baghshah, Ehsaneddin Asgari
TL;DR
LLMs’ reliance on static parametric memory contributes to hallucinations and outdated knowledge, motivating multimodal RAG for richer external grounding. The survey synthesizes methods, resources, evaluation, applications, and open problems across multimodal retrieval and generation, while noting unresolved robustness, alignment, and coverage limitations.
Problem
LLMs suffer from hallucinations and outdated knowledge because reliance on parametric memory limits access to up-to-date, verifiable information, while multimodal RAG introduces additional alignment and reasoning challenges.
Method
The survey comprehensively reviews multimodal RAG formulation, datasets, benchmarks, applications, retrieval, fusion, augmentation, generation, training, loss functions, and agent frameworks.
Results
The survey organizes recent multimodal RAG advances and evaluates the field’s datasets, benchmarks, metrics, applications, and methodological trends.
Takeaways & Limitations
The survey identifies cross-modal reasoning, retrieval, unified embedding spaces, and agent-based interaction as promising directions for multimodal RAG research.
Takeaways & Limitations
Descriptions are necessarily concise, the literature selection may omit emerging or domain-specific work, and the survey does not comparatively evaluate models because task settings and metrics vary.
Abstract
from arXiv · showhide
Large Language Models (LLMs) suffer from hallucinations and outdated knowledge due to their reliance on static training data. Retrieval-Augmented Generation (RAG) mitigates these issues by integrating external dynamic information for improved factual grounding. With advances in multimodal learning, Multimodal RAG extends this approach by incorporating multiple modalities such as text, images, audio, and video to enhance the generated outputs. However, cross-modal alignment and reasoning introduce unique challenges beyond those in unimodal RAG. This survey offers a structured and comprehensive analysis of Multimodal RAG systems, covering datasets, benchmarks, metrics, evaluation, methodologies, and innovations in retrieval, fusion, augmentation, and generation. We review training strategies, robustness enhancements, loss functions, and agent-based approaches, while also exploring the diverse Multimodal RAG scenarios. In addition, we outline open challenges and future directions to guide research in this evolving field. This survey lays the foundation for developing more capable and reliable AI systems that effectively leverage multimodal dynamic external knowledge bases. All resources are publicly available at https://github.com/llm-lab-org/Multimodal-RAG-Survey.
1 Introduction & Background
Multimodal RAG extends retrieval-augmented generation to heterogeneous data, improving contextual grounding while introducing cross-modal alignment and reasoning challenges. This survey organizes recent methods, resources, and open directions for the field.
- LLMs face hallucinations, outdated knowledge, and limited access to verifiable information because they rely on parametric memory.
- RAG addresses these limitations by retrieving external knowledge and incorporating it into generation to improve factual grounding.
- Multimodal RAG incorporates images, audio, and structured data alongside text to enrich contextual grounding and generation.
- Multimodal RAG improves reasoning through cross-modal cues but must handle modality selection, fusion, and cross-modal alignment.
- Multimodal RAG Formulation: In the formulation, modality-specific encoders map documents into a shared semantic space, retrieval selects relevant context, and a generator produces the response.
- The survey reviews task formulation, datasets, benchmarks, applications, and innovations across retrieval, fusion, augmentation, generation, training, losses, and agents.
2 Datasets, Evaluation, and Applications
Multimodal RAG evaluation spans diverse tasks, applications, datasets, and metrics, reflecting the need to assess retrieval, generation, and modality alignment together.
- Multimodal RAG supports tasks including multimodal summarization, visual question answering, and video understanding.
- Applications extend across healthcare, software engineering, fashion, entertainment, and emerging domains.
- Evaluation requires multiple metrics covering retrieval performance, generation quality, and modality alignment.
3 Key Innovations and Methodologies
The survey groups multimodal RAG innovations across retrieval, document and modality-specific processing, fusion, augmentation, and generation. These methods seek better cross-modal matching, context selection, integration, and response quality.
- Retrieval Strategy: Unified embedding spaces enable direct cross-modal retrieval, while adaptive quantization and hybrid retrieval address efficiency and recall trade-offs.
- Retrieval Strategy: Text-centric retrieval remains foundational, complemented by image, video, audio, and document methods tailored to modality-specific structure.
- Retrieval Strategy: Re-ranking and filtering improve candidate quality through optimized selection, relevance scoring, hard negatives, consensus, and dynamic modality filtering.
- Fusion and Alignment: Fusion methods align heterogeneous representations using textual conversion, interleaved inputs, cross-attention, co-attention, and cross-modality tying.
- Augmentation: Augmentation refines retrieved data through context enrichment and iterative coarse-to-fine retrieval before generation.
- Generation Techniques: Instruction tuning trains generation components to use question-conditioned visual features, dynamic prompts, adaptive retrieval, preference signals, and source evidence.
4 Open Problems and Future Directions
The survey identifies robustness, explainability, cross-modal reasoning, alignment, retrieval bias, and interactive agent behavior as unresolved challenges. It highlights unified representations and feedback-driven systems as future directions.
- Generalization, Explainability, and Robustness: Multimodal RAG systems remain vulnerable to domain shift, modality bias, imprecise attribution, adversarial perturbations, and low-quality or outdated sources.
- Reasoning, Alignment, and Retrieval Enhancement: Compositional reasoning, modality alignment, entity-aware retrieval, and knowledge-graph integration remain insufficiently developed.
- Reasoning, Alignment, and Retrieval Enhancement: Retrieval faces position sensitivity, redundancy, and training-data or retrieved-content biases, while unified multimodal embedding spaces remain open research challenges.
- Agent-Based and Self-Guided Systems: Future agent-based systems should combine retrieval, reasoning, generation, interactive feedback, and self-guided decisions to iteratively refine outputs.
5 Conclusion
The survey organizes multimodal RAG research across retrieval, fusion, augmentation, generation, training, and agents, while identifying applications, evaluation resources, and future research directions.
- The survey categorizes advances in retrieval, multimodal fusion, augmentation, generation, training strategies, and agent-based approaches.
- It examines task-specific applications, datasets, benchmarks, and evaluation methods for multimodal RAG systems.
- The survey highlights cross-modal reasoning, retrieval, agent-based interaction, and unified multimodal embedding spaces as future research directions.
6 Limitations
The survey’s scope is constrained by concise methodology descriptions, potentially incomplete coverage, absent comparative evaluation, and the rapidly evolving nature of multimodal RAG.
- Space constraints require the survey to describe individual methodologies concisely.
- The literature selection may overlook emerging or domain-specific studies despite coverage of major venues and recent work.
- The survey does not comparatively evaluate models because tasks, metrics, implementation details, and computational requirements vary substantially.
- Its taxonomy and conclusions may evolve as multimodal RAG develops and new paradigms emerge.
7 Ethical Statement
The survey identifies ethical risks in multimodal RAG, including bias, misinformation, privacy, and intellectual property concerns, and emphasizes transparent mitigation and evaluation.
- Bias may arise during retrieval and generation, producing skewed or unfair outputs.
- Retrieval failures or unreliable sources can cause multimodal systems to hallucinate or propagate misinformation.
- Sensitive multimodal data creates privacy risks, while generated content raises attribution and copyright-compliance concerns.
- Addressing these concerns requires careful dataset curation, bias mitigation, and transparent evaluation of retrieval and generation mechanisms.
B Dataset and Benchmark
Multimodal RAG research uses varied datasets and benchmarks spanning modalities, domains, and evaluation goals, but persistent gaps remain in bias, noise, coverage, robustness, integration, and any-to-any support.
- Benchmarks evaluate visual reasoning, external knowledge integration, dynamic retrieval, retrieval relevance, robustness, and OCR-related error effects.
- Large web-crawled datasets provide scale and diversity but may introduce noise, whereas curated datasets prioritize annotation quality and domain-specific detail.
- Datasets span image–text, video–text, audio–text, medical, fashion, 3D, knowledge-and-QA, and other categories.
- Existing resources remain limited by societal bias, noisy annotations, domain-specific coverage, and insufficient real-world or long-context complexity.
- Multimodal benchmarks lack sufficient adversarial examples and structured negative instances for broad robustness testing.
- Many evaluations separate retrieval from generation, motivating holistic assessment of retrieved context, relevance, and final output quality.
- Current datasets primarily support text, image, video, and audio rather than comprehensive any-to-any modality interactions.
C Evaluation and Metrics
Multimodal RAG evaluation combines retrieval, generation, modality-alignment, efficiency, and domain-specific metrics because systems process varied input types and have complex structures.
- Evaluation Overview: About 60 metrics are used to evaluate multimodal RAG across retrieval, generation, and modality capabilities.The review combines metrics from vision-language models, generative AI, and retrieval systems.
- Retrieval Evaluation: Retrieval evaluation uses accuracy, recall, precision, F1, Top-K Accuracy, Recall@K, and Mean Reciprocal Rank.Recall@K focuses on relevant items among the top K results, while MRR measures the rank of the first relevant result.
- Modality Evaluation: Text-generation evaluation includes Exact Match, BLEU, ROUGE, METEOR, and multilingual ROUGE, while image captioning uses CIDEr and SPICE.ROUGE-N measures n-gram overlap, and ROUGE-L measures the longest common subsequence between generated and reference text.
- Modality Evaluation: Image quality is evaluated with FID and KID, while audio quality and relevance can be assessed with human ratings and Fréchet Audio Distance.FID compares feature distributions of real and generated images; human ratings assess sound quality and text relevance.
- Efficiency and Domain Metrics: System efficiency uses FLOPs, execution time, response time, and retrieval time per query, with domain metrics such as geodesic distance and Clinical Relevance.These metrics address computational performance and application-specific evaluation needs.
D.1 Robustness and Noise Management
Multimodal RAG robustness methods address noisy retrieval inputs and modality-specific biases through noise-aware training, knowledge filtering, and contrastive or adversarial objectives.
- Noise Management: Multimodal training must manage noise and modality-specific biases because irrelevant retrieval inputs can degrade model performance.MORE injects irrelevant results during training to improve focus on relevant inputs.
- Noise Management: RA-BLIP filters relevant knowledge for generation with Adaptive Selection Knowledge Generation and a denoising-enhanced loss term.The approach avoids fine-tuning and reduces computational overhead by minimizing trainable parameters while achieving strong baseline performance.
- Noise Management: RagVL improves robustness by combining hard-negative samples at the data level with Gaussian noise and loss reweighting at the token level.This design applies noise injection at multiple stages of training.
- Loss Functions: InfoNCE is used in contrastive learning, with positive-pair embeddings and a temperature parameter defining the loss inputs.The supplied formulation identifies zi and zj as embeddings of a positive pair and τ as the temperature parameter.
- Loss Functions: GAN loss separates discriminator and generator objectives, whereas Triplet Loss pulls similar points together and pushes dissimilar points apart in embedding space.The GAN formulation uses real samples, generated samples, noise vectors, and discriminator probabilities; Triplet Loss uses anchor, positive, and negative samples.
E Applications and Relevant Tasks
Multimodal RAG supports cross-modal generation, knowledge-intensive applications, and diverse domain deployments, while evaluation spans retrieval, generation quality, and modality alignment.
- Relevant Tasks: Multimodal RAG enhances image captioning, text-to-image synthesis, visual storytelling, and factual alignment in multimodal summarization.These applications retrieve contextual information to support cross-modal content generation.
- Healthcare and Medicine: Healthcare systems combine medical images, electronic health records, biomedical literature, and patient data for diagnosis, report drafting, multilingual support, risk assessment, and federated integration.Examples include MMED-RAG, FactMM-RAG, AsthmaBot, Realm, and privacy-preserving clinical architectures.
- Software Engineering: Software-engineering systems retrieve documentation, API specifications, debugging patterns, code diffs, and repository history for code completion and commit-message generation.DocPrompting, RACE, and CEDAR illustrate retrieval use in code-related tasks.
- Fashion and E-Commerce: Fashion and e-commerce systems jointly use garment images and text for style-aware retrieval, query expansion, product discovery, and retrieval-augmented image editing.UniFashion, Dang, and Fashion-RAG represent these cross-modal applications.
- Entertainment and Emerging Applications: Entertainment, autonomous systems, wireless networks, maintenance, and geospatial applications link heterogeneous signals for tactical analysis, navigation, resilience, diagnosis, and geolocalization.Examples include SoccerRAG, RAG-Driver, ENWAR, equipment-maintenance retrieval, and Img2Loc.
- Challenges: Long-context multimodal RAG faces high video-sampling costs, multi-page document memory bottlenecks, fixed frame-extraction limits, and retrieval speed-accuracy trade-offs.The discussion motivates adaptive frame selection and more efficient scalable processing.