Uncertainty Quantification for Multimodal Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2605.29956v1
• Date: Thu, 28 May 2026 14:00:32 GMT
• Title: Uncertainty Quantification for Multimodal Retrieval Augmented Generation
• Abstract summary: We show that modeling uncertainty using multimodal and retrieval-aware probability signals improves estimation in multimodal RAG systems.<n>Our proposed LeMUQ increases the AUROC metric by 3.8% on average.<n>Our findings highlight the importance of modeling multimodal uncertainty and provide a step toward more reliable and safer multimodal RAG systems.
• Score: 14.789439938448034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval Augmented Generation (RAG) improves the question answering capabilities of Large Language Models (LLMs) by incorporating external knowledge and has recently been extended to multimodal settings through Vision-Language Models (VLMs) that integrate visual and textual information. Despite these advances, generated answers can still be incorrect or misleading. Uncertainty Quantification (UQ) methods aim to estimate the reliability of model outputs, but most existing approaches are designed for text-only models and perform poorly in multimodal RAG scenarios. A key challenge is capturing uncertainty arising from multiple stages of the pipeline, including retrieval, visual understanding, and generation. In this work, we show that modeling uncertainty using multimodal and retrieval-aware probability signals improves estimation in multimodal RAG systems. We introduce LeMUQ, a Learnable Multimodal UQ method that analyzes token probabilities under input modifications, such as removing modalities or retrieved context. By encoding these signals as probability tokens and processing them with a finetuned model, our approach captures interactions between modalities and retrieval. Experiments across datasets, retrievers, and VLMs show consistent improvements over baseline and finetuned UQ methods. Our proposed LeMUQ increases the AUROC metric by 3.8% on average. Additionally, our method shows strong generalization performance across different retrieval setups and datasets with mixed results when transferring across different VLMs. Our findings highlight the importance of modeling multimodal uncertainty and provide a step toward more reliable and safer multimodal RAG systems. Code is available on GitHub.

Related papers

• Evolution of Multimodal Question Answering: From Modality-Adaptive Extraction to Unified Language Representation [0.0]
  We compare Multimodal Adaptive Extraction (MAE), Solar, and UniMMQA, tracing the evolution of multimodal question answering.<n>Our analysis demonstrates a shift from explicit modality-specific processing toward unified text-centric formulations.<n>Despite these advances, we identify persistent challenges, including information loss during modality transformation, error propagation in multi-stage pipelines, and limitations in capturing fine-grained cross-modal dependencies.
  arXiv  Detail & Related papers  (2026-09-08T15:34:00Z)
• Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs [78.09559830840595]
  We present the first systematic study on quantizing diffusion-based language models.<n>We identify the presence of activation outliers, characterized by abnormally large activation values.<n>We implement state-of-the-art PTQ methods and conduct a comprehensive evaluation.
  arXiv  Detail & Related papers  (2025-08-20T17:59:51Z)
• FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering [21.142461103887857]
  We propose a new benchmark FRAMES-VQA (Fine-Tuning Robustness across Multi-Modal Shifts in VQA) for evaluating robust fine-tuning for VQA tasks.<n>We utilize ten existing VQA benchmarks, including VQAv2, IV-VQA, VQA-CP, OK-VQA and others, and categorize them into ID, near and far OOD datasets.
  arXiv  Detail & Related papers  (2025-05-27T20:44:44Z)
• Benchmarking Retrieval-Augmented Multimodal Generation for Document Question Answering [60.062194349648195]
  Document Visual Question Answering (DocVQA) faces dual challenges in processing lengthy multimodal documents.<n>Current document retrieval-augmented generation (DocRAG) methods remain limited by their text-centric approaches.<n>We introduce MMDocRAG, a comprehensive benchmark featuring 4,055 expert-annotated QA pairs with multi-page, cross-modal evidence chains.
  arXiv  Detail & Related papers  (2025-05-22T09:52:57Z)
• VLMT: Vision-Language Multimodal Transformer for Multimodal Multi-hop Question Answering [8.21219588747224]
  This paper introduces Vision-Language Multimodal Transformer (VLMT), a unified architecture that integrates a vision encoder with a sequence-to-sequence language model.<n>VLMT employs a direct token-level injection mechanism to fuse visual and textual inputs within a shared embedding space.<n> Comprehensive experiments on two benchmark datasets demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2025-04-11T05:51:44Z)
• Knowledge-Aware Iterative Retrieval for Multi-Agent Systems [0.0]
  We introduce a novel large language model (LLM)-driven agent framework.<n>It iteratively refines queries and filters contextual evidence by leveraging dynamically evolving knowledge.<n>The proposed system supports both competitive and collaborative sharing of updated context.
  arXiv  Detail & Related papers  (2025-03-17T15:27:02Z)
• Benchmarking Retrieval-Augmented Generation in Multi-Modal Contexts [56.7225771305861]
  This paper introduces Multi-Modal Retrieval-Augmented Generation (M$2$RAG), a benchmark designed to evaluate the effectiveness of Multi-modal Large Language Models.<n>The benchmark comprises four tasks: image captioning, multi-modal question answering, multi-modal fact verification, and image reranking.<n>To enhance the context utilization capabilities of MLLMs, we also introduce Multi-Modal Retrieval-Augmented Instruction Tuning (MM-RAIT)
  arXiv  Detail & Related papers  (2025-02-24T16:25:25Z)
• Ask in Any Modality: A Comprehensive Survey on Multimodal Retrieval-Augmented Generation [2.549112678136113]
  Retrieval-Augmented Generation (RAG) mitigates issues by integrating external dynamic information for improved factual grounding.<n>Cross-modal alignment and reasoning introduce unique challenges beyond those in unimodal RAG.<n>This survey lays the foundation for developing more capable and reliable AI systems.
  arXiv  Detail & Related papers  (2025-02-12T22:33:41Z)
• Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency [66.96286531087549]
  Uncertainty quantification (UQ) methods for Large Language Models (LLMs) encompass a variety of approaches.<n>We propose a novel approach to integrating model confidence with output consistency, resulting in a family of efficient and robust UQ methods.<n>We evaluate our approach across various tasks such as question answering, abstractive summarization, and machine translation.
  arXiv  Detail & Related papers  (2025-02-07T14:30:12Z)
• Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
  Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs)<n>We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs.<n>We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
  arXiv  Detail & Related papers  (2024-12-19T13:25:39Z)
• CUE-M: Contextual Understanding and Enhanced Search with Multimodal Large Language Model [9.224965304457708]
  This paper introduces Contextual Understanding and Enhanced Search with MLLM (CUE-M), a novel multimodal search framework.<n>It incorporates image context enrichment, intent refinement, contextual query generation, external API integration, and relevance-based filtering.<n>Experiments on real-word datasets and public benchmarks on knowledge-based VQA and safety demonstrated that CUE-M outperforms baselines and establishes new state-of-the-art results.
  arXiv  Detail & Related papers  (2024-11-19T07:16:48Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)

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.