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Seeing Through the MiRAGE: Evaluating Multimodal Retrieval Augmented Generation

large-language-models › multimodal-llms

📄 Abstract

Abstract: We introduce MiRAGE, an evaluation framework for retrieval-augmented generation (RAG) from multimodal sources. As audiovisual media becomes a prevalent source of information online, it is essential for RAG systems to integrate information from these sources into generation. However, existing evaluations for RAG are text-centric, limiting their applicability to multimodal, reasoning intensive settings because they don't verify information against sources. MiRAGE is a claim-centric approach to multimodal RAG evaluation, consisting of InfoF1, evaluating factuality and information coverage, and CiteF1, measuring citation support and completeness. We show that MiRAGE, when applied by humans, strongly aligns with extrinsic quality judgments. We additionally introduce automatic variants of MiRAGE and three prominent TextRAG metrics -- ACLE, ARGUE, and RAGAS -- demonstrating the limitations of text-centric work and laying the groundwork for automatic evaluation. We release open-source implementations and outline how to assess multimodal RAG.

Authors (8)

Alexander Martin

William Walden

Reno Kriz

Dengjia Zhang

Kate Sanders

Eugene Yang

+2 more

Submitted

October 28, 2025

arXiv Category

cs.CL

arXiv PDF Code

Key Contributions

MiRAGE is a novel evaluation framework for retrieval-augmented generation (RAG) from multimodal sources, addressing the limitations of text-centric evaluations. It introduces InfoF1 (factuality and information coverage) and CiteF1 (citation support and completeness) metrics, demonstrating strong alignment with human judgments and highlighting the shortcomings of existing text-centric RAG metrics.

Business Value

Enables more robust and reliable multimodal AI systems, crucial for applications dealing with diverse online content. This leads to better information synthesis, more trustworthy AI-generated content, and improved user trust.

Paper Metadata

Innovation Type

Evaluation Framework/Methodology

Deployment Feasibility

High, as it's an evaluation framework that can be applied to existing multimodal RAG systems.

Limitations Addressed

Existing RAG evaluations are text-centric and do not adequately assess systems that retrieve information from multimodal sources (e.g., images, videos).

Performance Gains

Demonstrated strong alignment of MiRAGE metrics with human judgments.,Showcased limitations of text-centric RAG metrics for multimodal RAG.

View Code on GitHub

Technical Tags

Multimodal RAGRetrieval-Augmented GenerationEvaluation frameworkAudiovisual mediaFactualityCitation supportInfoF1CiteF1TextRAG metrics

Research Topics

Multimodal AIRetrieval-Augmented GenerationAI EvaluationInformation VerificationGenerative Models

Methods & Architectures

Claim-centric evaluationInfoF1 metricCiteF1 metricHuman evaluationAutomatic evaluation Multimodal RAG systems

Applications & Tasks

Information retrieval Content generation Multimedia analysis Fact-checking Text-centric RAG evaluationInability to evaluate multimodal sourcesLack of factuality verification in RAG Evaluating multimodal RAG systemsMeasuring factuality and citation supportDeveloping new RAG evaluation metrics

Related Fields

Multimodal AINatural Language ProcessingInformation RetrievalComputer VisionMachine Learning Evaluation

Keywords

Multimodal RAGRetrieval-Augmented GenerationEvaluationFrameworkFactualityCitationAudiovisualLLMMiRAGEInfoF1CiteF1TextRAGMultimedia

Academic Context

#Multimodal AI#Retrieval-Augmented Generation#AI Evaluation#Information Verification#Generative Models

Commercial Potential

Potential Products

Evaluation tools for multimodal AIFact-checking systems for multimedia contentRAG systems with enhanced verification

Target Industries

Media and EntertainmentTechnologyInformation ServicesEducation

Use Case Examples

Evaluating an AI system that generates summaries of news videos, ensuring factual accuracy and proper source citation.Assessing a multimodal chatbot that answers questions based on product images and descriptions.

Competitive Edge

Provides the first dedicated evaluation framework for multimodal RAG, filling a critical gap in assessing systems that integrate information from diverse media types.

Market Opportunity

Rapid growth in multimodal AI and RAG applications.

Revenue Models

Consulting serviceslicensing of evaluation tools.

Resource Requirements

Compute Needs

Moderate (for running evaluation metrics)

Data Requirements

Multimodal datasets with associated claims and sources

Deployment Constraints

Requires careful setup of evaluation pipelines and potentially human annotators.

Scalability

Scalability depends on the efficiency of the implemented evaluation metrics and the size of the test data.

Production Readiness

Maturity Level

Research/Development

Time to Market

6-12 months

Licensing

Open-source

Patent Potential

Low (evaluation framework)

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