Update README.md

README.md

@@ -1,52 +1,13 @@
----
-license: other
-license_name: license
-license_link: LICENSE
-configs:
-- config_name: default
-  data_files:
-  - split: standard
-    path: data/standard-*
-  - split: high_quality
-    path: data/high_quality-*
-dataset_info:
-  features:
-  - name: level
-    dtype: int32
-  - name: language
-    dtype: string
-  - name: text_only
-    dtype: bool
-  - name: image
-    dtype: image
-  - name: id
-    dtype: string
-  - name: text
-    dtype: string
-  - name: label
-    dtype: string
-  splits:
-  - name: standard
-    num_bytes: 4548122261.668258
-    num_examples: 108677
-  - name: high_quality
-    num_bytes: 1699405334.9488075
-    num_examples: 40617
-  download_size: 5773349370
-  dataset_size: 6247527596.617065
----
-
-# Multilingual Reasoning Kangaroo Math Problems 1.0 (M3Kang)
+
+# M3Kang: A Multilingual Multimodal Mathematical Reasoning Dataset with Kangaroo Problems
 ## Introduction
 
-Despite state-of-the-art vision-language models (VLMs) have demonstrated strong reasoning capabilities, their performance in multilingual mathematical reasoning remains underexplored, particularly when compared to human performance. To bridge this gap, we introduce M3Kang, the first massively multilingual, multimodal mathematical reasoning dataset for VLMs. It is derived from the Kangaroo Math Competition, the world’s largest mathematics contest, which annually engages over six million participants under the age of 18 across more than 90 countries. M3Kang includes 1,789 unique multiple-choice problems organized by grade-level difficulty, with translations into more than 100 culturally diverse languages, for a total of more than 50K problems.
+Despite state-of-the-art vision-language models (VLMs) have demonstrated strong reasoning capabilities, their performance in multilingual mathematical reasoning remains underexplored. To bridge this gap, we introduce M3Kang, the first massively multilingual, multimodal mathematical reasoning dataset for VLMs. It is derived from the Kangaroo Math Competition, the world’s largest mathematics contest, which annually engages over six million participants under the age of 18 across more than 90 countries. M3Kang includes 1,747 unique multiple-choice problems organized by grade-level difficulty, with translations into 108 culturally diverse languages. The dataset consists of two splits: standard, used in the paper for benchmarking and containing 108,677 problems, and high-quality, a smaller subset of problems with higher-quality translations, totaling 40,617 problems.
 
-This dataset is published as part of our paper “M3Kang: Evaluating Multilingual Multimodal Mathematical Reasoning in Vision-Language Models”.
+This dataset is published as part of our paper [M3Kang: Evaluating Multilingual Multimodal Mathematical Reasoning in Vision-Language Models](https://openreview.net/pdf?id=txURffKsPo).
 
 ## Description
-The M3Kang dataset is built from problems in the Kangaroo Math Competition, the largest annual international contest for primary and secondary students. Problems are organized by grade level and sometimes accompanied by figures (see “Sample Images” below for an example). We sourced data from the original PDFs of the 2007–2024 editions organized by the Catalan Math Society in Catalonia, Spain, and processed it through a pipeline described below. In addition to processing and formatting the problems into a proper data set structure compatible with Hugging Face, our processing pipeline automatically translates the problems to many other languages, to obtain the final multilingual data set. 
-
-The final dataset includes each problem in two formats: a text version of the question and an image containing the full problem (question, multi-choice answers, and optional figure, and the correct answer) with translations into more than 100 languages.
+The M3Kang dataset is built from problems in the Kangaroo Math Competition, the largest annual international contest for primary and secondary students. Problems are organized by grade level and sometimes accompanied by figures (see “Sample Images” below for an example). We sourced data from the original PDFs of the 2007–2024 editions organized by the Catalan Math Society in Catalonia, Spain, and processed it through a pipeline described below. In addition to processing and formatting the problems into a proper data set structure compatible with Hugging Face, our processing pipeline automatically translates the problems to many other languages and performs quality checks on the translations, to obtain the final multilingual data set. The final dataset includes each problem in two formats: a text version of the question and an image containing the full problem (question, multi-choice answers, and optional figure, and the correct answer) with translations into more than 100 languages.
 
 ## Sample Images
 Example of problem found in the M3Kang dataset, with translations to English, Catalan, Spanish, and German:
@@ -58,29 +19,25 @@ Example of problem found in the M3Kang dataset, with translations to English, Ca
 | | |
 |---|---|
 | Type of problems | Math, logic, reasoning |
-| Number of English problems	 | 1789 |
+| Number of English problems	 | 1747 |
 | Multimodal? | Yes, text + image |
-| Multilingual?	| Yes, more than 100 languages (final number will be provided) |
+| Multilingual?	| Yes, it contains tranlsations to 108 languages |
 | Number of levels | 8 levels: from school grade 5 through grade 12 |
-| Total # of problems | More than 50K (final number will be provided) |
-
-(*) Note that because our translation pipeline includes a quality assurance stage that discards low quality translations, the number of problems in non-English languages is in general smaller than 1789.
-
-For an example, please see the section “Data Format” below.
+| Total # of problems | 108,677 |
 
-## Dataset Collection Process
-
-We sourced data from the original PDFs of the 2007–2024 editions organized by the Catalan Math Society (SCM) in Catalonia, Spain. We gained access to this dataset following formal approval from the SCM, who authorized our use of the data for research purposes via an official letter.
+Note that because our translation pipeline includes a quality assurance stage that discards low quality translations, the number of problems in non-English languages is in general smaller than 1747. See the following table for the exact number of problems in each language for both splits. 
 
-The original data was available in the form of PDF files, each file containing a test of math multi-choice Kangaroo problems (e.g., a test with 24 problems, for a given year and a given school level). The input data also included the correct answer to each problem.
-
-We developed a pipeline to process the set of input PDFs and automatically (with some human-in-the-loop support) generate the data set in a format compatible with Hugging Face. The pipeline used to generate the dataset includes only software that has meticulously approved by the legal team. This pipeline is also described in our paper “M3Kang: Evaluating Multilingual Multimodal Mathematical Reasoning in Vision-Language Models”.
+![m3kang_datasetoverview](https://cdn-uploads.huggingface.co/production/uploads/68e7940f7b9f8592b18752b2/7ydZqSIkObbrrDEhwSHIU.png)
 
 ## Data Format
 A sample from our dataset consists in a string of “text”, containing the problem statement, an “image”, containing a snapshot of the problem (which includes the problem statement, the answer key and any accompanying figures), an “answer” field, containing the correct answer, a “language” field, containing the language of the problem, and a “level”, containing the level (school grade) of the problem. For example:
 
 ```json
 {
+  "id: "lvl-6_2007_10",
+  “level”: 6,
+  “language”: “eng”,
+  "text_only": false,
   “text”: “In the figure on the right, you can
     see a triangle ABC in which two segments have
     been drawn from vertices A and B to points on
@@ -90,22 +47,37 @@ A sample from our dataset consists in a string of “text”, containing the pro
     four from A and four from B, into how many
     non-overlapping regions will the triangle be
     divided?”,
+  "label": "B",
   “image”: 
 ```
 ![picture2](https://cdn-uploads.huggingface.co/production/uploads/68e7940f7b9f8592b18752b2/RDAoGgOcntNXg70E50_-5.jpeg)
-
 ```json
-  “answer”: “B”,
-  “language”: “eng”,
-  “level”: 6
 }
 ```
 
+## Dataset Collection Process
+
+We sourced data from the original PDFs of the 2007–2024 editions organized by the Catalan Math Society (SCM) in Catalonia, Spain. We gained access to this dataset following formal approval from the SCM, who authorized our use of the data for research purposes via an official letter. The original data was available in the form of PDF files, each file containing a test of math multi-choice Kangaroo problems (e.g., a test with 24 problems, for a given year and a given school level). The input data also included the correct answer to each problem.
+
+We developed a pipeline to process the set of input PDFs and automatically (with some human-in-the-loop support) generate the data set in a format compatible with Hugging Face. The pipeline used to generate the dataset includes only software that has meticulously approved by the legal team. This pipeline is also described in the paper [M3Kang: Evaluating Multilingual Multimodal Mathematical Reasoning in Vision-Language Models](https://openreview.net/pdf?id=txURffKsPo).
+
 ## Dataset license
 This dataset is intended for research purposes only. See [Data License Agreement - Research Use](LICENSE.pdf)
 
 ## Dataset Citation Instructions
-Please cite our paper if you use this dataset in your research. A link to the publication will soon be provided.
+Please cite our paper if you use this dataset in your research.
+
+```bibtex
+@inproceedings{
+anonymous2025mkang,
+title={M3Kang: Evaluating Multilingual Multimodal Mathematical Reasoning in Vision-Language Models},
+author={Anonymous},
+booktitle={Submitted to The Fourteenth International Conference on Learning Representations},
+year={2025},
+url={https://openreview.net/forum?id=txURffKsPo},
+note={under review}
+}
+```
 
 ### Qualcomm AI Research
 At Qualcomm AI Research, we are advancing AI to make its core capabilities – perception, reasoning, and action – ubiquitous across devices. Our mission is to make breakthroughs in fundamental AI research and scale them across industries. By bringing together some of the best minds in the field, we’re pushing the boundaries of what’s possible and shaping the future of AI.