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README.md

@@ -1,41 +1,139 @@
 ---
 dataset_info:
   features:
-  - name: image
-    dtype: image
-  - name: chart_type
-    dtype: string
-  - name: question
-    dtype: string
-  - name: question_class
-    dtype: string
-  - name: gold
-    list: string
-  - name: correct_or_misleading
-    dtype: int64
-  - name: misleader
-    dtype: string
-  - name: annotations
-    list: string
+    - name: image
+      dtype: image
+    - name: chart_type
+      dtype: string
+    - name: question_class
+      dtype: string
+    - name: gold
+      sequence: string
+    - name: correct_or_misleading
+      dtype: int64
+    - name: misleader
+      dtype: string
+    - name: annotations
+      sequence: string
   splits:
-  - name: train
-    num_bytes: 227281966
-    num_examples: 6059
-  - name: val
-    num_bytes: 232938718
-    num_examples: 6146
-  - name: test
-    num_bytes: 222234422
-    num_examples: 6042
-  download_size: 736621719
-  dataset_size: 682455106
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: val
-    path: data/val-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 6059
+    - name: val
+      num_examples: 6146
+    - name: test
+      num_examples: 6042
+tags:
+  - charts
+  - data-visualization
+  - visual-question-answering
+  - image-classification
+  - misleading-visualizations
+  - arxiv:2601.12983
+task_categories:
+  - image-classification
+  - visual-question-answering
+pretty_name: AttackViz
 ---
+
+# AttackViz
+
+AttackViz is a chart-image dataset for studying correct and misleading data visualizations. It was introduced in the paper [ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation](https://arxiv.org/abs/2601.12983).
+
+Each example contains a rendered chart image, metadata about the chart and question type, the expected gold answer, a binary label indicating whether the chart is correct or misleading, a misleading-visualization category, and serialized chart annotations.
+
+## Dataset Structure
+
+The dataset contains 18,247 examples across three splits:
+
+| Split | Examples |
+|---|---:|
+| train | 6,059 |
+| val | 6,146 |
+| test | 6,042 |
+
+## Fields
+
+- `image`: chart image.
+- `chart_type`: chart family. Values include `v_bar`, `h_bar`, and `line`.
+- `question_class`: question category. Values include `compound`, `comparison`, `min_max`, `data_retrieval`, and `arithmetic`.
+- `gold`: list of expected answer strings.
+- `correct_or_misleading`: binary label where `0` means correct and `1` means misleading.
+- `misleader`: misleading visualization type, or `none` for correct charts.
+- `annotations`: list of serialized JSON strings containing the underlying chart specification and rendering metadata.
+
+## Chart Types
+
+| Type | Examples |
+|---|---:|
+| v_bar | 7,414 |
+| h_bar | 7,348 |
+| line | 3,485 |
+
+## Question Classes
+
+| Class | Examples |
+|---|---:|
+| compound | 4,581 |
+| comparison | 4,258 |
+| min_max | 4,172 |
+| data_retrieval | 3,989 |
+| arithmetic | 1,247 |
+
+## Label Distribution
+
+| Label | Meaning | Examples |
+|---:|---|---:|
+| 0 | correct | 6,373 |
+| 1 | misleading | 11,874 |
+
+## Misleading Types
+
+| Type | Examples |
+|---|---:|
+| none | 6,373 |
+| inappropriate_use_of_stacked | 2,689 |
+| misrepresentation | 1,694 |
+| inverted_axis | 1,674 |
+| inappropriate_axis_range | 1,370 |
+| 3d | 1,276 |
+| inappropriate_use_of_log_scale | 942 |
+| inappropriate_use_of_line | 548 |
+| truncated_axis | 498 |
+| ineffective_color_scheme | 498 |
+| inappropriate_item_order | 460 |
+| dual_axis | 225 |
+
+## Loading
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("jgermanmx/AttackViz")
+print(dataset)
+print(dataset["train"][0])
+```
+
+## Intended Use
+
+This dataset can be used to evaluate or train models for chart understanding, misleading visualization detection, visual question answering over charts, and analysis of how design choices affect interpretation.
+
+## Citation
+
+If you use AttackViz, please cite:
+
+```bibtex
+@misc{ortizbarajas2026chartattack,
+  title = {ChartAttack: Testing the Vulnerability of LLMs to Malicious Prompting in Chart Generation},
+  author = {Jesus-German Ortiz-Barajas and Jonathan Tonglet and Vivek Gupta and Iryna Gurevych},
+  year = {2026},
+  eprint = {2601.12983},
+  archivePrefix = {arXiv},
+  primaryClass = {cs.CL},
+  doi = {10.48550/arXiv.2601.12983},
+  url = {https://arxiv.org/abs/2601.12983}
+}
+```
+
+## Notes
+
+The `annotations` field stores JSON as strings. Parse individual entries with `json.loads` when structured access to chart specifications is needed.