# Localizing Before Answering: A Hallucination Evaluation Benchmark for Grounded Medical Multimodal LLMs

arXiv: https://arxiv.org/abs/2505.00744 · HTML: https://mlsanity.com/paper/2505.00744/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 34 of its 49 references that are in the map.
- First posted on arXiv 30 Apr 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Hallucination mitigation in LVLMs](https://mlsanity.com/mllm/hallucination-mitigation-in-lvlms/)

## Builds on (most cited first, 30 of 34)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [LISA: Reasoning Segmentation via Large Language Model](https://mlsanity.com/paper/2308.00692/) (2023)
- [Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding](https://mlsanity.com/paper/2311.16922/) (2023)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation](https://mlsanity.com/paper/2311.17911/) (2023)
- [SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering](https://mlsanity.com/paper/2102.09542/) (2021)
- [Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning](https://mlsanity.com/paper/2306.14565/) (2023)
- [Med-Flamingo: a Multimodal Medical Few-shot Learner](https://mlsanity.com/paper/2307.15189/) (2023)
- [PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering](https://mlsanity.com/paper/2305.10415/) (2023)
- [VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations](https://mlsanity.com/paper/2012.15029/) (2020)
- [Multi-Modal Hallucination Control by Visual Information Grounding](https://mlsanity.com/paper/2403.14003/) (2024)
- [OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM](https://mlsanity.com/paper/2402.09181/) (2024)
- [Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data](https://mlsanity.com/paper/2308.02463/) (2023)
- [MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology](https://mlsanity.com/paper/2301.02228/) (2023)
- [Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training](https://mlsanity.com/paper/2403.02325/) (2024)
- [CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models](https://mlsanity.com/paper/2406.06007/) (2024)
- [MedBLIP: Bootstrapping Language-Image Pre-training from 3D Medical Images and Texts](https://mlsanity.com/paper/2305.10799/) (2023)
- [VCoder: Versatile Vision Encoders for Multimodal Large Language Models](https://mlsanity.com/paper/2312.14233/) (2023)
- [Prompt Highlighter: Interactive Control for Multi-Modal LLMs](https://mlsanity.com/paper/2312.04302/) (2023)
- [Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding](https://mlsanity.com/paper/2210.03416/) (2022)
- [Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQA](https://mlsanity.com/paper/2405.20421/) (2024)

## Built on by (most cited first, 14 of 14)

- [Instruction-Guided Lesion Segmentation for Chest X-rays with Automatically Generated Large-Scale Dataset](https://mlsanity.com/paper/2511.15186/) (2025)
- [Seeing the Trees for the Forest: Rethinking Weakly-Supervised Medical Visual Grounding](https://mlsanity.com/paper/2505.15123/) (2025)
- [CXReasonAgent: Evidence-Grounded Diagnostic Reasoning Agent for Chest X-rays](https://mlsanity.com/paper/2602.23276/) (2026)
- [Foundation Models in Biomedical Imaging: Turning Hype into Reality](https://mlsanity.com/paper/2512.15808/) (2025)
- [SSMNBench: Diagnosing Image-based Cross-View Human-Object Understanding via Single-View Sufficiency and Multi-View Necessity](https://mlsanity.com/paper/2606.25634/) (2026)
- [The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy](https://mlsanity.com/paper/2607.11175/) (2026)
- [CoTBox-TTT: Grounding Medical VQA with Visual Chain-of-Thought Boxes During Test-time Training](https://mlsanity.com/paper/2511.12446/) (2025)
- [Colon-X: Advancing Intelligent Colonoscopy toward Clinical Reasoning](https://mlsanity.com/paper/2512.03667/) (2025)
- [Seeing Before Agreeing: Aligning Multi-Agent Consensus with Visual Evidence](https://mlsanity.com/paper/2605.30698/) (2026)
- [CRAFT: Causal Responsibility and Failure Tracing in Medical Vision Language Models](https://mlsanity.com/paper/2609.38810/) (2026)
- [MedVIGIL: Evaluating Trustworthy Medical VLMs Under Broken Visual Evidence](https://mlsanity.com/paper/2605.07919/) (2026)
- [Rad-VLSM: A Cross-Modal Framework with Semantics-Assisted Prompting for Medical Segmentation and Diagnosis](https://mlsanity.com/paper/2605.18130/) (2026)
- [SHOVIR: A Benchmark for Evaluating Vision Shortcut Learning in Radiology Report Generation](https://mlsanity.com/paper/2606.30201/) (2026)
- [PROVE: Proof-guided Regime-aware Operator Verification for Hallucination Detection in Medical Visual Question Answering](https://mlsanity.com/paper/2609.29604/) (2026)

## Abstract (arXiv)

Medical Large Multi-modal Models (LMMs) have demonstrated remarkable capabilities in medical data interpretation. However, these models frequently generate hallucinations contradicting source evidence, particularly due to inadequate localization reasoning. This work reveals a critical limitation in current medical LMMs: instead of analyzing relevant pathological regions, they often rely on linguistic patterns or attend to irrelevant image areas when responding to disease-related queries. To address this, we introduce HEAL-MedVQA (Hallucination Evaluation via Localization MedVQA), a comprehensive benchmark designed to evaluate LMMs' localization abilities and hallucination robustness. HEAL-MedVQA features (i) two innovative evaluation protocols to assess visual and textual shortcut learning, and (ii) a dataset of 67K VQA pairs, with doctor-annotated anatomical segmentation masks for pathological regions. To improve visual reasoning, we propose the Localize-before-Answer (LobA) framework, which trains LMMs to localize target regions of interest and self-prompt to emphasize segmented pathological areas, generating grounded and reliable answers. Experimental results demonstrate that our approach significantly outperforms state-of-the-art biomedical LMMs on the challenging HEAL-MedVQA benchmark, advancing robustness in medical VQA.