README.md

---
language:
- en
license: other
pretty_name: Geo Benchmark
task_categories:
- text-generation
configs:
- config_name: GKMC
  data_files:
  - split: test
    path: GKMC/test-*
- config_name: GeoQuery_place
  data_files:
  - split: train
    path: GeoQuery_place/train-*
  - split: validation
    path: GeoQuery_place/validation-*
  - split: test
    path: GeoQuery_place/test-*
- config_name: GeoQuery_regression
  data_files:
  - split: train
    path: GeoQuery_regression/train-*
  - split: validation
    path: GeoQuery_regression/validation-*
  - split: test
    path: GeoQuery_regression/test-*
- config_name: GeoQuestions1089_YN
  data_files:
  - split: test
    path: GeoQuestions1089_YN/test-*
- config_name: GeoQuestions1089_coord
  data_files:
  - split: test
    path: GeoQuestions1089_coord/test-*
- config_name: GeoQuestions1089_place
  data_files:
  - split: test
    path: GeoQuestions1089_place/test-*
- config_name: GeoQuestions1089_regression
  data_files:
  - split: test
    path: GeoQuestions1089_regression/test-*
- config_name: GeoSQA
  data_files:
  - split: train
    path: GeoSQA/train-*
  - split: validation
    path: GeoSQA/validation-*
  - split: test
    path: GeoSQA/test-*
- config_name: GridRoute
  data_files:
  - split: test
    path: GridRoute/test-*
- config_name: MsMarco
  data_files:
  - split: test
    path: MsMarco/test-*
  - split: train
    path: MsMarco/train-*
  - split: validation
    path: MsMarco/validation-*
- config_name: NY-POI
  data_files:
  - split: test
    path: NY-POI/test-*
- config_name: PPNL_multi
  data_files:
  - split: test
    path: PPNL_multi/test-*
  - split: train
    path: PPNL_multi/train-*
  - split: validation
    path: PPNL_multi/validation-*
- config_name: PPNL_single
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  - split: test
    path: PPNL_single/test-*
  - split: train
    path: PPNL_single/train-*
  - split: validation
    path: PPNL_single/validation-*
- config_name: SpartUN
  data_files:
  - split: test
    path: SpartUN/test-*
  - split: train
    path: SpartUN/train-*
  - split: validation
    path: SpartUN/validation-*
- config_name: SpatialEvalLLM
  data_files:
  - split: test
    path: SpatialEvalLLM/test-*
- config_name: TourismQA
  data_files:
  - split: test
    path: TourismQA/test-*
  - split: train
    path: TourismQA/train-*
  - split: validation
    path: TourismQA/validation-*
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- config_name: GKMC
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    dtype: int64
  - name: answer
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  - name: scenario
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  - name: C
    dtype: string
  - name: D
    dtype: string
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    num_examples: 1600
  download_size: 510919
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- config_name: GeoQuery_place
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  - name: answer
    list: string
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  - name: validation
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    num_examples: 33
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    num_examples: 184
  download_size: 30317
  dataset_size: 89876
- config_name: GeoQuery_regression
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  - name: question
    dtype: string
  - name: answer
    list: float64
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    num_examples: 182
  - name: validation
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    num_examples: 17
  - name: test
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    num_examples: 89
  download_size: 13105
  dataset_size: 19009
- config_name: GeoQuestions1089_YN
  features:
  - name: question_id
    dtype: int64
  - name: question
    dtype: string
  - name: answer
    list: bool
  - name: answer_type
    list: string
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  download_size: 7718
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- config_name: GeoQuestions1089_coord
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  - name: question
    dtype: string
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    list:
      list: float64
  - name: answer_type
    list: string
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  download_size: 6242
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  - name: question
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  - name: answer_type
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    num_examples: 455
  download_size: 1896109
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- config_name: GeoQuestions1089_regression
  features:
  - name: question_id
    dtype: int64
  - name: question
    dtype: string
  - name: answer
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  - name: answer_type
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  - name: test
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    num_examples: 231
  download_size: 10620
  dataset_size: 20755
- config_name: GeoSQA
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  - name: scenario_id
    dtype: int64
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  - name: question
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  - name: A
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  - name: C
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    num_examples: 628
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  download_size: 1327080
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- config_name: GridRoute
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  - name: start
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  - name: end
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  - name: path
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      list: int64
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    num_examples: 300
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- config_name: MsMarco
  features:
  - name: question_id
    dtype: int64
  - name: question
    dtype: string
  - name: answer
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    num_examples: 4149
  download_size: 58502647
  dataset_size: 117726201
- config_name: NY-POI
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    list:
      list: string
  - name: recent_check-ins
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      list: string
  - name: candidates
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  - name: ground_truth
    list: string
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  - name: test
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    num_examples: 1347
  download_size: 3829756
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- config_name: PPNL_multi
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  - name: world_description
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  - name: world
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  - name: obstacles_coords
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  - name: start
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  - name: end
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      list: int64
  - name: n_goals
    dtype: int64
  - name: path
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      list: int64
  - name: agent_as_a_point
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  - name: agent_has_direction
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  - name: distribution
    dtype: string
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    num_examples: 55440
  - name: train
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    num_examples: 53440
  - name: validation
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    num_examples: 6680
  download_size: 13201821
  dataset_size: 166536744
- config_name: PPNL_single
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- config_name: SpartUN
  features:
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  - name: question_id
    dtype: string
  - name: scenario
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  - name: question
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  - name: k_hop
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    num_examples: 37095
  - name: validation
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    num_examples: 5600
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  dataset_size: 31592330
- config_name: SpatialEvalLLM
  features:
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: struct_type
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  - name: size
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  - name: k_hop
    dtype: string
  - name: seed
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  - name: description_level
    dtype: string
  splits:
  - name: test
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    num_examples: 1400
  download_size: 211349
  dataset_size: 1091123
- config_name: TourismQA
  features:
  - name: question
    dtype: string
  - name: city
    struct:
    - name: coord
      list: float64
    - name: name
      dtype: string
  - name: tagged_locations
    list: string
  - name: tagged_locations_lat_long
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  - name: answers_names
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  - name: answers_adresses
    list: string
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  - name: validation
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  dataset_size: 89826009
---

# 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