Add task category and link to paper

Adds `task_categories` metadata field and links the paper.

README.md

@@ -45,16 +45,19 @@ configs:
     path: data/visual_metaphor-*
   - split: visual_basic
     path: data/visual_basic-*
-
+task_categories:
+- table-question-answering
+language:
+- en
 ---
 
+# InfoChartQA: Benchmark for Multimodal Question Answering on Infographic Charts
 
-
-# InfoChartQA:  Benchmark for Multimodal Question Answering on Infographic Charts
+[Paper](https://arxiv.org/abs/2505.19028)
 
 🤗[Dataset](https://huggingface.co/datasets/Jietson/InfoChartQA)
 
-# Dataset 
+# Dataset
 You can find our dataset on huggingface: 🤗[InfoChartQA Dataset](https://huggingface.co/datasets/Jietson/InfoChartQA)
 
 # Usage
@@ -89,11 +92,14 @@ You should store and evaluate model's response as:
 def build_question(query):#to build the question
     question = ""
     if "prompt" in query:
-    	question = question + f"{query["prompt"]}\n"
-    question = question + f"{query["question"]}\n"
+    	question = question + f"{query["prompt"]}
+"
+    question = question + f"{query["question"]}
+"
     if "options" in query:
         for _ in query["options"]:
-        	question = question + f"{_} {query['options'][_]}\n"
+        	question = question + f"{_} {query['options'][_]}
+"
 	if "instructions" in query:
     	question = question + query["instructions"]
     return question
@@ -116,6 +122,71 @@ from checker import evaluate
 evaluate("model_reponse.json", "path_to_save_the_result")
 ```
 
+Or simply use after your answer is generated:
+
+```python
+python -c "import checker; checker.evaluate(sys.argv[1], sys.argv[2])" PATH_TO_INPUT_FILE PATH_TO_INPUT_FILE 
+```
+
+# LeaderBoard
+
+| Model                        | Infographic | Plain   | Δ     | Basic  | Metaphor | Avg.   | 
+|------------------------------|-------------|---------|-------|--------|----------|--------| 
+| **Baselines**                |             |         |       |        |          |        | 
+| Human                        | 95.35\*      | 96.28\*  | 0.93  | 93.17\*| 88.69    | 90.93  | 
+| **Proprietary Models**       |             |         |       |        |          |        | 
+| OpenAI O4-mini               | 79.41       | 94.61   | 15.20 | 92.12  | 54.76    | 73.44  | | GPT-4.1                      | 70.01       | 83.36   | 13.35 | 88.47  | 50.87    | 69.67  | 
+| GPT-4o                       | 66.09       | 81.77   | 15.68 | 81.77  | 47.19    | 64.48  | 
+| Claude 3.5 Sonnet            | 65.67       | 83.11   | 17.44 | 90.36  | 55.33    | 72.85  | 
+| Gemini 2.5 Pro Preview       | 83.31       | 93.88   | 10.07 | 90.01  | 60.42    | 75.22  | 
+| Gemini 2.5 Flash Preview     | 71.91       | 84.66   | 12.75 | 82.02  | 56.28    | 69.15  | 
+| **Open-Source Models**       |             |         |       |        |          |        | 
+| Qwen2.5-VL-72B               | 62.06       | 78.47   | 16.41 | 77.34  | 54.64    | 65.99  | 
+| Llama-4 Scout                | 67.41       | 84.84   | 17.43 | 81.76  | 51.89    | 66.83  | 
+| Intern-VL3-78B               | 66.38       | 82.18   | 15.80 | 79.46  | 51.52    | 65.49  | 
+| Intern-VL3-8B                | 56.82       | 73.50   | 16.68 | 74.26  | 49.57    | 61.92  | 
+| Janus Pro                    | 29.61       | 45.29   | 15.68 | 41.18  | 42.21    | 41.69  | 
+| DeepSeek VL2                 | 39.81       | 47.01   | 7.20  | 58.72  | 44.54    | 51.63  | 
+| Phi-4                        | 46.20       | 66.97   | 20.77 | 61.87  | 38.31    | 50.09  | 
+| LLaVA OneVision Chat 78B     | 47.78       | 63.66   | 15.88 | 62.11  | 50.22    | 56.17  | 
+| LLaVA OneVision Chat 7B      | 38.41       | 54.43   | 16.02 | 61.03  | 45.67    | 53.35  | 
+| Pixtral                      | 44.70       | 60.88   | 16.11 | 64.23  | 50.87    | 57.55  | 
+| Ovis1.6-Gemma2-9B            | 50.56       | 64.52   | 13.98 | 60.96  | 34.42    | 47.69  | 
+| ChartGemma                   | 19.99       | 33.81   | 13.82 | 30.52  | 33.77    | 32.15  | 
+| TinyChart                    | 26.34       | 44.73   | 18.39 | 14.72  | 9.03     | 11.88  | 
+| ChartInstruct-LLama2         | 20.55       | 27.91   | 7.36  | 33.86  | 33.12    | 33.49  |
+
+# License
+
+Our original data contributions (all data except the charts) are distributed under the [CC BY-SA 4.0](https://github.com/princeton-nlp/CharXiv/blob/main/data/LICENSE) license. Our code is licensed under [Apache 2.0](https://github.com/princeton-nlp/CharXiv/blob/main/LICENSE) license. The copyright of the charts belong to the original authors.
 
+## Paper Links
 
- 
+### 📌 Main Paper (This Repository)
+
+- **[InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts](https://arxiv.org/abs/2505.19028)**  
+  _Minzhi Lin, Tianchi Xie, Mengchen Liu, Yilin Ye, Changjian Chen, Shixia Liu_  
+
+### Relevant Papers
+
+- **[OrionBench: A Benchmark for Chart and Human-Recognizable Object Detection in Infographics](https://arxiv.org/abs/2505.17473)**  Jiangning Zhu, Yuxing Zhou, Zheng Wang, Juntao Yao, Yima Gu, Yuhui Yuan, Shixia Liu_  
+
+- **[ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation](https://arxiv.org/abs/2505.18668)**  
+  _Zhen Li, Duan Li, Yukai Guo, Xinyuan Guo, Bowen Li, Lanxi Xiao, Shenyu Qiao, Jiashu Chen, Zijian Wu, Hui Zhang, Xinhuan Shu, Shixia Liu_  
+
+
+## Cite
+
+If you use our work and are inspired by our work, please consider cite us (available soon):
+
+```
+@misc{lin2025infochartqabenchmarkmultimodalquestion,
+      title={InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts}, 
+      author={Minzhi Lin and Tianchi Xie and Mengchen Liu and Yilin Ye and Changjian Chen and Shixia Liu},
+      year={2025},
+      eprint={2505.19028},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2505.19028}, 
+}
+```