Add dataset card and link to paper

README.md

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+---
+task_categories:
+- image-text-to-text
+---
+
+# MVI-Bench
+
+MVI-Bench is a comprehensive benchmark specifically designed to evaluate the robustness of Large Vision-Language Models (LVLMs) against misleading visual inputs.
+
+- **Paper:** [MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs](https://huggingface.co/papers/2511.14159)
+- **Repository:** [https://github.com/chenyil6/MVI-Bench](https://github.com/chenyil6/MVI-Bench)
+
+## Introduction
+
+Grounded in fundamental visual primitives, the design of MVI-Bench centers on three hierarchical levels of misleading visual inputs:
+1. **Visual Concept**
+2. **Visual Attribute**
+3. **Visual Relationship**
+
+Using this taxonomy, the benchmark compiles 1,248 expertly annotated VQA instances across six representative categories. It also introduces **MVI-Sensitivity**, a novel metric that characterizes LVLM robustness at a granular level. Empirical results across 18 state-of-the-art LVLMs uncover pronounced vulnerabilities to misleading visual inputs.
+
+## Dataset Structure
+
+The benchmark evaluates how misleading visual information affects model understanding and decision-making in visual question-answering tasks. It covers 1,248 instances designed to test models against various visual misinformation scenarios across different levels of complexity.