rfr2003/Geo_Benchmark

Dataset Card for Geo-Benchmark

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
• Knowledge/Yes|No questions: GeoQuestions1089
• Knowledge/Regression questions: GeoQuestions1089, GeoQuery
• Knowledge/Place Prediction: GeoQuestions1089, GeoQuery, Ms Marco
• Reasoning/Scenario Complex QA: GeoSQA, GKMC
• Reasoning/Spatial Reasoning: SpartUN, StepGame, SpatialEvalLLM
• Application/POI Recommendation: TourismQA, NY-QA
• Application/Path Finding: GridRoute, PPNL

These datasets have been preprocessed in order to be easily accessible.

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 GeoQuery	– 180	– 17	234 88
	Place prediction	GeoQuestions1089 GeoQuery MS-Marco	– 348 23 513	– 32 4 149	455 184 2 907
──────────	──────────	──────────	──────────	──────────	──────────
Reasoning	Scenario Complex QA	GeoSQA GKMC	– –	– –	4 110 1 600
	Spatial Reasoning	SpatialEvalLLM SpartUN StepGame	– 37 095 50 000	– 5 600 5 000	1 400 5 551 100 000
──────────	──────────	──────────	──────────	──────────	──────────
Application	POI Recommendation	TourismQA NY-QA	19 960 –	2 119 –	2 173 1 347
	Path Finding	bAbI (task 19) GridRoute PPNL	9 000 – 69 472	1 000 – 8 684	1 000 300 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.

@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