Update dataset card for Deep Learning for Geometry Problem Solving Survey

README.md

@@ -1,53 +1,27 @@
 ---
-license: apache-2.0
 language:
 - en
-- zh
-- es
-- fr
-- pt
-- ko
+license: mit
+pretty_name: Deep Learning for Geometry Problem Solving Survey
+task_categories:
+- any-to-any
 tags:
-- Multilingual
-- Multimodal
-- Cognitive Science
-- General Intelligence Ability Benchmark
-pretty_name: M3GIA
-size_categories:
-- 1K<n<10K
-configs:
-- config_name: chinese
-  data_files:
-  - split: test
-    path: chinese_v1.parquet
-- config_name: english
-  data_files:
-  - split: test
-    path: english_v1.parquet
-- config_name: spanish
-  data_files:
-  - split: test
-    path: spanish_v1.parquet
-- config_name: french
-  data_files:
-  - split: test
-    path: french_v1.parquet
-- config_name: portuguese
-  data_files:
-  - split: test
-    path: portuguese_v1.parquet
-- config_name: korean
-  data_files:
-  - split: test
-    path: korean_v1.parquet
+- geometry
+- mathematical-reasoning
+- survey
+- deep-learning
+- multimodal
 ---
 
-# M3GIA: A Cognition Inspired Multilingual and Multimodal General Intelligence Ability 
+# Deep Learning for Geometry Problem Solving (DL4GPS)
 
-[**🌐 Homepage**] | [**🤗 Dataset**](https://huggingface.co/datasets/Songweii/M3GIA/) | [**🤗 Paper**](https://arxiv.org/abs/2406.05343) | [**📖 arXiv**](https://arxiv.org/abs/2406.05343) | [**💻 GitHub**](https://github.com/songweii/M3GIA/tree/main)
+This repository serves as a continuously updated reading list for the survey paper:
 
-The evaluation code can be found in [**💻 GitHub**](https://github.com/songweii/M3GIA/tree/main).
+[**A Survey of Deep Learning for Geometry Problem Solving**](https://huggingface.co/papers/2507.11936)
 
-[**Abstract**]
+**Abstract:**
+Geometry problem solving is a key area of mathematical reasoning, which is widely involved in many important fields such as education, mathematical ability assessment of artificial intelligence, and multimodal ability assessment. In recent years, the rapid development of deep learning technology, especially the rise of multimodal large language models, has triggered a widespread research boom. This paper provides a survey of the applications of deep learning in geometry problem solving, including (i) a comprehensive summary of the relevant tasks in geometry problem solving; (ii) a thorough review of related deep learning methods; (iii) a detailed analysis of evaluation metrics and methods; and (iv) a critical discussion of the current challenges and future directions that can be explored. Our goal is to provide a comprehensive and practical reference of deep learning for geometry problem solving to promote further developments in this field. We create a continuously updated list of papers on GitHub: this https URL .
 
-As recent multi-modality large language models (MLLMs) have shown formidable proficiency on various complex tasks, there has been increasing attention on debating whether these models could eventually mirror human intelligence. However, existing benchmarks mainly focus on evaluating solely on task performance, such as the accuracy of identifying the attribute of an object. Combining well-developed cognitive science to understand the intelligence of MLLMs beyond superficial achievements remains largely unexplored. To this end, we introduce the first cognitive-driven multi-lingual and multi-modal benchmark to evaluate the general intelligence ability of MLLMs, dubbed M3GIA. Specifically, we identify five key cognitive factors based on the well-recognized Cattell-Horn-Carrol (CHC) model of intelligence and propose a novel evaluation metric. In addition, since most MLLMs are trained to perform on different languages, a nature question arises, is language a key factor to influence the cognitive ability of MLLMs? As such, we go beyond English to encompass other languages based on their popularity, including Chinese, French, Spanish, Portuguese and Korean, to construct our M3GIA. We make sure all the data relevant to the cultural backgrounds are collected from their native context to avoid English-centric bias. We collected a significant corpus of data from human participants, revealing that the most advanced MLLM reaches the lower boundary of human intelligence in English. Yet, there remains a pronounced disparity in the other five languages assessed. We also reveals an interesting 'winner takes all' phenomenon that are aligned with the discovery in cognitive studies. Our benchmark will be open-sourced, with the aspiration that it will facilitate the enhancement of cognitive capabilities in MLLMs.
+**GitHub Repository (Reading List):** [https://github.com/majianz/gps-survey](https://github.com/majianz/gps-survey)
+
+The GitHub repository provides a detailed table of contents, categorizing relevant papers on various tasks, datasets, and methods in deep learning for geometry problem solving.