RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models

• URL: http://arxiv.org/abs/2407.05131v2
• Date: Thu, 17 Oct 2024 01:55:28 GMT
• Title: RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models
• Authors: Peng Xia, Kangyu Zhu, Haoran Li, Hongtu Zhu, Yun Li, Gang Li, Linjun Zhang, Huaxiu Yao,
• Abstract summary: Current Medical Large Vision Language Models (Med-LVLMs) frequently encounter factual issues. RAG, which utilizes external knowledge, can improve the factual accuracy of these models but introduces two major challenges. We propose RULE, which consists of two components. First, we introduce a provably effective strategy for controlling factuality risk through the selection 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.
• Score: 35.60385437194243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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