RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models
Open accessAbstract
The recent emergence of Medical Large Vision Language Models (Med-LVLMs) has enhanced medical diagnosis.However, current Med-LVLMs frequently encounter factual issues, often generating responses that do not align with established medical facts.Retrieval-Augmented Generation (RAG), which utilizes external knowledge, can improve the factual accuracy of these models but introduces two major challenges.First, limited retrieved contexts might not cover all necessary information, while excessive retrieval can introduce irrelevant and inaccurate references, interfering with the model's generation.Second, in cases where the model originally responds correctly, applying RAG can lead to an over-reliance on retrieved contexts, resulting in incorrect answers.To address these issues, we propose RULE, which consists of two components.First, we introduce a provably effective strategy for controlling factuality risk through the calibrated selection of the number of retrieved contexts.Second, based on samples where over-reliance on retrieved contexts led to errors, we curate a preference dataset to fine-tune the model, balancing its dependence on inherent knowledge and retrieved contexts for generation.We demonstrate the effectiveness of RULE on medical VQA and report generation tasks across three datasets, achieving an average improvement of 47.4% in factual accuracy.We publicly release our benchmark and code in https: //github.com/richard-peng-xia/RULE.
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Known performance degradation with long or noisy textual input for medical vision-language large models.
“To address the known performance degradation with long or noisy textual input for medical vision-language large models83,84”
From MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling (2025) · cites this paper as [83]
Section 3.2 says excessive retrieved text can introduce low-relevance or inaccurate references that interfere with a medical vision-language model’s generation. Section 3.3 also reports cases where adding retrieved context turns a correct answer into an incorrect one. These findings support degradation from lengthy or unreliable retrieved context, but not a general claim about long or noisy textual input of any kind. (LLM, conf=0.86)
Experimental — accuracy not yet measuredKnown performance degradation with long or noisy textual input for medical vision-language large models.
“To address the known performance degradation with long or noisy textual input for medical vision-language large models83,84”
From MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling (2025) · cites this paper as [84]
The Medical Image section says that a large number of retrieved textual contexts can include irrelevant or inaccurate references that interfere with Med-LVLM generation. Section 3.3 also describes correct answers becoming incorrect after retrieved contexts are added. This supports degradation from noisy retrieved text, but the paper does not establish degradation from long textual input in general. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThis is similar to challenges observed in RAG systems where retrieved information is not effectively incorporated.
“This is similar to challenges observed in RAG systems where retrieved information is not effectively incorporated (50, 51).”
From Swin-Qwen3: a three-stage vision–language framework for automated radiology report generation with multi-agent verification (2026) · cites this paper as [50]
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- Authors
- Xia, P., Zhu, K., Li, H., Zhu, H., Li, Y., Li, G., Zhang, L., & Yao, H.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Brown University (US)
- Hong Kong Polytechnic University (HK)
- Rutgers, The State University of New Jersey (US)
- Venue
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Published
- 2024
- Pages
- 1081-1093
- OpenAlex
- W4404783798
- Cited by
- 67
- References
- 0
- Citations by year
- 2026: 32 · 2025: 32 · 2024: 3
- FWCI
- 13.88
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Cisco Systems
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- retrieval-augmented generation, factuality, factual accuracy, preference dataset, fine-tuning, medical report generation, multimodal retrieval
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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