Why does adding images and tables cut down on made-up facts?
Standard RAG systems only retrieve text, so they miss crucial information in charts, graphs, and tables—forcing the language model to guess about visual details, which leads to hallucinations. Multimodal RAG solves this by indexing both text and visual summaries into a single search space, so when you ask about a chart's color or a trend line, the system retrieves the actual image or its description. Paper [3] demonstrates this directly: their multimodal system reduced hallucinations related to chart colors and visual data trends because it could pull up the exact visual evidence instead of relying on the model's memory.
Paper [1] goes further by adding a 'feature enhancement layer' that fuses vision and text embeddings, making the retrieval more precise. On multimodal hallucination benchmarks, this approach significantly improved factual consistency compared to standard RAG. The key insight is that hallucinations often arise from missing or mismatched context—multimodal retrieval fills those gaps by giving the model both the words and the pictures.
How much better are multimodal systems at reducing hallucinations?
The most striking number comes from paper [2], which tested a multimodal-style RAG (MEGA-RAG) that combined text retrieval, keyword search, and biomedical knowledge graphs. It cut hallucination rates by over 40% compared to standard RAG and standalone LLMs, while also achieving the highest accuracy (79.13%) and F1 score (79.04%). This is a large, practical improvement—meaning nearly half of the factual errors that a standard RAG would make were eliminated.
Paper [1] reports similar gains on multimodal benchmarks, though without a single headline percentage. Together, these two studies [1][2] provide converging evidence: when you expand retrieval to include multiple evidence types (text, images, graphs, knowledge graphs), you consistently get fewer hallucinations than with text-only retrieval. The only caveat is that the exact improvement depends on the domain—public health and financial documents showed strong gains, while general-purpose benchmarks may see smaller effects.
Are there situations where standard RAG is just as good?
Yes—if your documents are purely text with no images, tables, or complex layouts, standard RAG may perform equally well. Paper [4] reviews the broader landscape and notes that hallucinations in RAG often stem from poor retrieval quality or misalignment between retrieved text and the query, not from a lack of visual data. In those cases, improving the retriever (e.g., using a small, well-trained retriever as in paper [5]) can reduce hallucinations without needing multimodal capabilities.
Paper [5] specifically shows that a standard RAG system with a compact retriever can match the performance of a larger LLM on structured outputs, cutting hallucinations while using fewer resources. So the choice hinges on your data: if your documents are multimodal (scanned forms, charts, infographics), multimodal RAG is clearly better. If they are plain text, investing in a better retriever may be more efficient than adding vision models.
About These Sources
This answer is built on 5 peer-reviewed studies — published from 2024 to 2026, 5 from 2024 or later, 2 in Q1 journals, collectively cited 66 times — selected as the most relevant from 5 studies that passed quality screening, drawn from 52 papers retrieved from a database of over 500 million.
Sources used in this answer
Structured Multimodal RAG: Document-Aware Retrieval and Alignment for Hallucination Mitigation
Proposes a multimodal RAG framework with structured document representation, hybrid retrieval, and vision-text fusion; on multimodal hallucination benchmarks it significantly improves factual consistency and reduces hallucination rates.
MEGA-RAG: a retrieval-augmented generation framework with multi-evidence guided answer refinement for mitigating hallucinations of LLMs in public health
MEGA-RAG integrates dense retrieval, keyword search, and biomedical knowledge graphs; in public health tasks it cut hallucination rates by over 40% and achieved 79.13% accuracy, outperforming standard RAG and standalone LLMs.
Multimodal RAG Based System to Handle Financial Documents
Develops a multimodal RAG system using LLaVA 1.5 VLM that indexes text and visual summaries together; reduces hallucinations related to chart colors and visual data trends by retrieving actual images and graphs.
Hallucination Mitigation for Retrieval-Augmented Large Language Models: A Review
A comprehensive review of hallucination causes and mitigation techniques in retrieval-augmented LLMs; identifies poor retrieval quality and misalignment as key sources, and surveys methods including detection and correction.
Reducing hallucination in structured outputs via Retrieval-Augmented Generation
Shows that a standard RAG system with a small, well-trained retriever can reduce hallucination in structured outputs and allow a smaller LLM to match larger model performance, without needing multimodal components.