Update README.md

README.md

@@ -5,18 +5,28 @@ size_categories:
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 viewer: false
 license: cc-by-nc-sa-4.0
+paper: https://arxiv.org/abs/2603.22368
+repository: https://github.com/Harsh-Lalai/Evaluating-Vision-Language-Models-on-Misleading-Data-Visualizations
+point_of_contact: lalaiharsh26@gmail.com
 ---
+
+## Dataset Description
+
+- **Repository:** https://github.com/Harsh-Lalai/Evaluating-Vision-Language-Models-on-Misleading-Data-Visualizations
+- **Paper:** https://arxiv.org/abs/2603.22368
+- **Point of Contact:** lalaiharsh26@gmail.com
+
 # Evaluating Vision-Language Models on Misleading Data Visualizations (Dataset)
 
 ## Overview
 
 This dataset accompanies the paper:
 
-“When Visuals Aren’t the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations.”
+“[When Visuals Aren’t the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations.](https://arxiv.org/abs/2603.22368)”
 
-The dataset is designed to evaluate whether Vision-Language Models (VLMs) can detect misleading information in **data visualization-caption pairs**, and whether they can correctly attribute the source of misleadingness to appropriate error types: Caption-level reasoning errors and Visualization design errors.
+MisVisBench is designed to evaluate whether Vision-Language Models (VLMs) can detect misleading information in **data visualization-caption pairs**, and whether they can correctly attribute the source of misleadingness to appropriate error types: Caption-level reasoning errors and Visualization design errors.
 
-Unlike prior benchmarks that primarily focus on chart understanding or visual distortions, this dataset enables **fine-grained analysis of misleadingness arising from both textual reasoning and visualization design choices**.
+Unlike prior benchmarks that primarily focus on chart understanding or visual distortions, MisVisBench enables **fine-grained analysis of misleadingness arising from both textual reasoning and visualization design choices**.
 
 ---
 
@@ -324,9 +334,10 @@ For any issues related to the dataset, feel free to reach out to lalaiharsh26@gm
 # Citation
 
 ```
-@article{lalai2026misleadingvlm,
-  title={When Visuals Aren’t the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations},
+@article{lalai2026visuals,
+  title={When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations},
   author={Lalai, Harsh Nishant and Shah, Raj Sanjay and Pfister, Hanspeter and Varma, Sashank and Guo, Grace},
+  journal={arXiv preprint arXiv:2603.22368},
   year={2026}
 }
 ```