Adding README structure with first informations

README.md

@@ -407,4 +407,362 @@ dataset_info:
   dataset_size: 89826009
 ---
 
-# Geo-Benchmark
+# Dataset Card for Geo-Benchmark
+
+## Table of Contents
+
+## Dataset Description
+
+- **Homepage:** https://github.com/Rfr2003/GeoBenchmark
+- **Repository:** https://github.com/Rfr2003/GeoBenchmark
+- **Paper:** 
+- **Point of Contact:** rodrigo.ferreira-rodrigues@utoulouse.fr
+
+### Dataset Summary
+
+Geo-Benchmark aims to assess Large Language Models' (LLM) geographical abilities across a multitude of tasks. It is built from 12 datasets split across 8 differents tasks:
+
+- Knowledge/**Coordinates Prediction** : [GeoQuestions1089](https://github.com/AI-team-UoA/GeoQuestions1089)
+- Knowledge/**Yes|No questions**: [GeoQuestions1089](https://github.com/AI-team-UoA/GeoQuestions1089)
+- Knowledge/**Regression questions**: [GeoQuestions1089](https://github.com/AI-team-UoA/GeoQuestions1089), [GeoQuery](https://www.cs.utexas.edu/~ml/nldata/geoquery.html)
+- Knowledge/**Place Prediction**: [GeoQuestions1089](https://github.com/AI-team-UoA/GeoQuestions1089), [GeoQuery](https://www.cs.utexas.edu/~ml/nldata/geoquery.html), [Ms Marco](https://microsoft.github.io/msmarco/)
+- Reasoning/**Scenario Complex QA**: [GeoSQA](http://ws.nju.edu.cn/gaokao/geosqa/1.0/), [GKMC](https://github.com/nju-websoft/Jeeves-GKMC)
+- Reasoning/**Spatial Reasoning**: [SpartUN](https://github.com/HLR/SpaRTUN), [StepGame](https://github.com/ShiZhengyan/StepGame), [SpatialEvalLLM](https://github.com/runopti/SpatialEvalLLM)
+- Application/**POI Recommendation**: [TourismQA](https://github.com/dair-iitd/TourismQA), [NY-QA](https://sites.google.com/site/yangdingqi/home/foursquare-dataset)
+- Application/**Path Finding**: [GridRoute](https://github.com/LinChance/GridRoute), [PPNL](https://github.com/MohamedAghzal/llms-as-path-planners)
+
+These datasets have been preprocessed in order to be easily accessible.
+
+
+```python
+import datasets
+
+dataset = datasets.load_dataset("rfr2003/Geo_Benchmark", "GeoSQA")
+```
+
+### Supported Tasks and Leaderboards
+
+The dataset is used for Text Generation.
+
+### Languages
+
+All datasets are in English (`en`).
+
+## Dataset Structure
+
+As this dataset contains very heterogenous tasks, almost every dataset as a different data structure. 
+
+### Data Instances
+
+TO DO
+
+### Data Fields
+
+TO DO
+
+### Data Splits
+
+| Category        | Tasks                  | Datasets                                 | Train                 | Dev                 | Test                      |
+| --------------- | ---------------------- | ---------------------------------------- | --------------------- | ------------------- | ------------------------- |
+| **Knowledge**   | Coordinates Prediction | GeoQuestions1089                         | –                     | –                   | 84                        |
+|                 | Yes/No questions       | GeoQuestions1089                         | –                     | –                   | 181                       |
+|                 | Regression             | GeoQuestions1089<br>GeoQuery             | –<br>180              | –<br>17             | 234<br>88                 |
+|                 | Place prediction       | GeoQuestions1089<br>GeoQuery<br>MS-Marco | –<br>348<br>23 513    | –<br>32<br>4 149    | 455<br>184<br>2 907       |
+| **──────────**  | **──────────**         | **──────────**                           | **──────────**        | **──────────**      | **──────────**            |
+| **Reasoning**   | Scenario Complex QA    | GeoSQA<br>GKMC                           | –<br>–                | –<br>–              | 4 110<br>1 600            |
+|                 | Spatial Reasoning      | SpatialEvalLLM<br>SpartUN<br>StepGame    | –<br>37 095<br>50 000 | –<br>5 600<br>5 000 | 1 400<br>5 551<br>100 000 |
+| **──────────**  | **──────────**         | **──────────**                           | **──────────**        | **──────────**      | **──────────**            |
+| **Application** | POI Recommendation     | TourismQA<br>NY-QA                       | 19 960<br>–           | 2 119<br>–          | 2 173<br>1 347            |
+|                 | Path Finding           | bAbI (task 19)<br>GridRoute<br>PPNL      | 9 000<br>–<br>69 472  | 1 000<br>–<br>8 684 | 1 000<br>300<br>74 484    |
+| **──────────**  | **──────────**         | **──────────**                           | **──────────**        | **──────────**      | **──────────**            |
+| **Total**       | –                      | –                                        | **236 290**           | **29 942**          | **176 628**               |
+
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+[Needs More Information]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[Needs More Information]
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+[Needs More Information]
+
+#### Who are the annotators?
+
+[Needs More Information]
+
+### Personal and Sensitive Information
+
+[Needs More Information]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[Needs More Information]
+
+### Discussion of Biases
+
+[Needs More Information]
+
+### Other Known Limitations
+
+[Needs More Information]
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+[Needs More Information]
+
+### Citation Information
+
+Thanks for all the authors of the all the datasets. If you use this Benchmark, please cite their work too.
+
+```Tex
+@misc{huang2021retrieverreadermeetsscenariobasedmultiplechoice,
+      title={When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions}, 
+      author={Zixian Huang and Ao Wu and Yulin Shen and Gong Cheng and Yuzhong Qu},
+      year={2021},
+      eprint={2108.13875},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2108.13875}, 
+}
+
+@inproceedings{finegan-dollak-etal-2018-improving,
+    title = "Improving Text-to-{SQL} Evaluation Methodology",
+    author = "Finegan-Dollak, Catherine  and
+      Kummerfeld, Jonathan K.  and
+      Zhang, Li  and
+      Ramanathan, Karthik  and
+      Sadasivam, Sesh  and
+      Zhang, Rui  and
+      Radev, Dragomir",
+    editor = "Gurevych, Iryna  and
+      Miyao, Yusuke",
+    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2018",
+    address = "Melbourne, Australia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P18-1033/",
+    doi = "10.18653/v1/P18-1033",
+    pages = "351--360",
+}
+
+@inproceedings{data-geography-original
+  dataset   = {Geography, original},
+  author    = {John M. Zelle and Raymond J. Mooney},
+  title     = {Learning to Parse Database Queries Using Inductive Logic Programming},
+  booktitle = {Proceedings of the Thirteenth National Conference on Artificial Intelligence - Volume 2},
+  year      = {1996},
+  pages     = {1050--1055},
+  location  = {Portland, Oregon},
+  url       = {http://dl.acm.org/citation.cfm?id=1864519.1864543},
+}
+
+@misc{huang2019geosqabenchmarkscenariobasedquestion,
+      title={GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level}, 
+      author={Zixian Huang and Yulin Shen and Xiao Li and Yuang Wei and Gong Cheng and Lin Zhou and Xinyu Dai and Yuzhong Qu},
+      year={2019},
+      eprint={1908.07855},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/1908.07855}, 
+}
+
+@misc{li2025gridroutebenchmarkllmbasedroute,
+      title={GridRoute: A Benchmark for LLM-Based Route Planning with Cardinal Movement in Grid Environments}, 
+      author={Kechen Li and Yaotian Tao and Ximing Wen and Quanwei Sun and Zifei Gong and Chang Xu and Xizhe Zhang and Tianbo Ji},
+      year={2025},
+      eprint={2505.24306},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2505.24306}, 
+}
+
+@article{DBLP:journals/corr/NguyenRSGTMD16,
+  author    = {Tri Nguyen and
+               Mir Rosenberg and
+               Xia Song and
+               Jianfeng Gao and
+               Saurabh Tiwary and
+               Rangan Majumder and
+               Li Deng},
+  title     = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},
+  journal   = {CoRR},
+  volume    = {abs/1611.09268},
+  year      = {2016},
+  url       = {http://arxiv.org/abs/1611.09268},
+  archivePrefix = {arXiv},
+  eprint    = {1611.09268},
+  timestamp = {Mon, 13 Aug 2018 16:49:03 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/NguyenRSGTMD16.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+@inbook{placequestions,
+author = {Hamzei, Ehsan and Li, Haonan and Vasardani, Maria and Baldwin, Timothy and Winter, Stephan and Tomko, Martin},
+year = {2020},
+month = {01},
+pages = {3-19},
+title = {Place Questions and Human-Generated Answers: A Data Analysis Approach},
+isbn = {978-3-030-14745-7},
+doi = {10.1007/978-3-030-14745-7_1}
+}
+
+@inproceedings{aghzal2024can,
+  title={Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning},
+  author={Aghzal, Mohamed and Plaku, Erion and Yao, Ziyu},
+  booktitle={ICLR 2024 Workshop on Large Language Model (LLM) Agents},
+  year={2024}
+}
+
+@inproceedings{mirzaee-kordjamshidi-2022-transfer,
+    title = "Transfer Learning with Synthetic Corpora for Spatial Role Labeling and Reasoning",
+    author = "Mirzaee, Roshanak  and
+      Kordjamshidi, Parisa",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.emnlp-main.413",
+    pages = "6148--6165",
+    abstract = "",
+}
+
+@article{yamada2023evaluating,
+    title={Evaluating Spatial Understanding of Large Language Models},
+    author={Yamada, Yutaro and Bao, Yihan and Lampinen, Andrew K and Kasai, Jungo and Yildirim, Ilker},
+    journal={Transactions on Machine Learning Research},
+    year={2024}
+}
+
+@inproceedings{10.1145/3459637.3482320,
+    author = {Contractor, Danish and Shah, Krunal and Partap, Aditi and Singla, Parag and Mausam, Mausam},
+    title = {Answering POI-recommendation Questions using Tourism Reviews},
+    year = {2021},
+    isbn = {9781450384469},
+    publisher = {Association for Computing Machinery},
+    address = {New York, NY, USA},
+    url = {https://doi.org/10.1145/3459637.3482320},
+    doi = {10.1145/3459637.3482320},
+    booktitle = {Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
+    pages = {281–291},
+    numpages = {11},
+    keywords = {large scale qa, poi-recommendation, question answering, real world task, tourism qa},
+    location = {Virtual Event, Queensland, Australia},
+    series = {CIKM '21}
+}
+
+
+@misc{li2024locationawaremodularbiencoder,
+      title={Location Aware Modular Biencoder for Tourism Question Answering}, 
+      author={Haonan Li and Martin Tomko and Timothy Baldwin},
+      year={2024},
+      eprint={2401.02187},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2401.02187}, 
+}
+
+@inproceedings{10.1007/978-3-031-47243-5_15,
+  title = {Benchmarking Geospatial Question Answering Engines Using the Dataset GeoQuestions1089},
+  author = {Sergios-Anestis Kefalidis, Dharmen Punjani, Eleni Tsalapati, 
+         Konstantinos Plas, Mariangela Pollali, Michail Mitsios, 
+         Myrto Tsokanaridou, Manolis Koubarakis and Pierre Maret},
+  booktitle = {The Semantic Web - {ISWC} 2023 - 22nd International Semantic Web Conference,
+            Athens, Greece, November 6-10, 2023, Proceedings, Part {II}},
+  year = {2023}
+}
+
+@inproceedings{stepGame2022shi,
+    title={StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts},
+    author={Shi, Zhengxiang and Zhang, Qiang and Lipani, Aldo},
+    volume={36},
+    url={https://ojs.aaai.org/index.php/AAAI/article/view/21383},
+    DOI={10.1609/aaai.v36i10.21383}, 
+    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+    year={2022},
+    month={Jun.},
+    pages={11321-11329}
+}
+
+@inproceedings{Yang_2022, series={SIGIR ’22},
+   title={GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation},
+   url={http://dx.doi.org/10.1145/3477495.3531983},
+   DOI={10.1145/3477495.3531983},
+   booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
+   publisher={ACM},
+   author={Yang, Song and Liu, Jiamou and Zhao, Kaiqi},
+   year={2022},
+   month=jul, pages={1144–1153},
+   collection={SIGIR ’22} 
+}
+   
+@ARTICLE{6844862,
+  author={Yang, Dingqi and Zhang, Daqing and Zheng, Vincent W. and Yu, Zhiyong},
+  journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems}, 
+  title={Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs}, 
+  year={2015},
+  volume={45},
+  number={1},
+  pages={129-142},
+  keywords={Tensile stress;Data models;Context modeling;Correlation;Hidden Markov models;Location based social networks;spatial;temporal;tensor factorization;user activity preference;Location based social networks;spatial;temporal;tensor factorization;user activity preference},
+  doi={10.1109/TSMC.2014.2327053}
+}
+
+@inproceedings{10.1145/3539618.3591770,
+    author = {Yan, Xiaodong and Song, Tengwei and Jiao, Yifeng and He, Jianshan and Wang, Jiaotuan and Li, Ruopeng and Chu, Wei},
+    title = {Spatio-Temporal Hypergraph Learning for Next POI Recommendation},
+    year = {2023},
+    isbn = {9781450394086},
+    publisher = {Association for Computing Machinery},
+    address = {New York, NY, USA},
+    url = {https://doi.org/10.1145/3539618.3591770},
+    doi = {10.1145/3539618.3591770},
+    booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
+    pages = {403–412},
+    numpages = {10},
+    keywords = {graph transformer, hypergraph, next poi recommendation},
+    location = {Taipei, Taiwan},
+    series = {SIGIR '23}
+}
+
+@INPROCEEDINGS{10605522,
+  author={Feng, Shanshan and Lyu, Haoming and Li, Fan and Sun, Zhu and Chen, Caishun},
+  booktitle={2024 IEEE Conference on Artificial Intelligence (CAI)}, 
+  title={Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation}, 
+  year={2024},
+  volume={},
+  number={},
+  pages={1530-1535},
+  keywords={Accuracy;Large language models;Computational modeling;Buildings;Chatbots;Cognition;Data models;LLMs;Next POI Recommendation;Zero-shot;Spatial-Temporal Data},
+  doi={10.1109/CAI59869.2024.00277}
+}
+
+```
+
+### Contributions
+
+TO DO