Update README.md and answer labels

README.md

@@ -1,5 +1,7 @@
 ---
 license: apache-2.0
+paperswithcode_id: marvel
+pretty_name: MARVEL (Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning)
 task_categories:
 - visual-question-answering
 language:
@@ -12,6 +14,7 @@ size_categories:
 ### Dataset Description
 
 MARVEL is a new comprehensive benchmark dataset that evaluates multi-modal large language models' abstract reasoning abilities in six patterns across five different task configurations, revealing significant performance gaps between humans and SoTA MLLMs.
+![image](./marvel_illustration.jpeg)
 
 ### Dataset Sources [optional]
 
@@ -27,6 +30,21 @@ Evaluations for multi-modal large language models' abstract reasoning abilities.
 
 The directory **images** keeps all images, and the file **marvel_labels.jsonl** provides annotations and explanations for all questions.
 
+### Fields
+
+- **id** is of ID of the question
+- **pattern** is the high-level pattern category of the question
+- **task_configuration** is the task configuration of the question
+- **avr_question** is the text of the AVR question
+- **answer** is the answer to the AVR question
+- **explanation** is the textual reasoning process to answer the question
+- **f_perception_question** is the fine-grained perception question
+- **f_perception_answer** is the answer to the fine-grained perception question
+- **f_perception_distractor** is the distractor of the fine-grained perception question
+- **c_perception_question_tuple** is a list of coarse-grained perception questions
+- **c_perception_answer_tuple** is a list of answers to the coarse-grained perception questions
+- **file** is the path to the image of the question
+
 ## Citation [optional]
 
 **BibTeX:**
@@ -37,4 +55,4 @@ The directory **images** keeps all images, and the file **marvel_labels.jsonl**
   journal={arXiv preprint arXiv:2404.13591},
   year={2024}
 }
-```
+```