--- 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 data_files: - 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-* dataset_info: - config_name: GKMC features: - name: question_id dtype: int64 - name: answer dtype: string - name: scenario dtype: string - name: question dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string splits: - name: test num_bytes: 1055828 num_examples: 1600 download_size: 510919 dataset_size: 1055828 - config_name: GeoQuery_place features: - name: question dtype: string - name: answer list: string splits: - name: train num_bytes: 57875 num_examples: 346 - name: validation num_bytes: 4037 num_examples: 33 - name: test num_bytes: 27964 num_examples: 184 download_size: 30317 dataset_size: 89876 - config_name: GeoQuery_regression features: - name: question dtype: string - name: answer list: float64 splits: - name: train num_bytes: 12026 num_examples: 182 - name: validation num_bytes: 1017 num_examples: 17 - name: test num_bytes: 5966 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 splits: - name: test num_bytes: 12412 num_examples: 181 download_size: 7718 dataset_size: 12412 - config_name: GeoQuestions1089_coord features: - name: question_id dtype: int64 - name: question dtype: string - name: answer list: list: float64 - name: answer_type list: string splits: - name: test num_bytes: 7042 num_examples: 87 download_size: 6242 dataset_size: 7042 - config_name: GeoQuestions1089_place features: - name: question_id dtype: int64 - name: question dtype: string - name: answer list: string - name: answer_type list: string splits: - name: test num_bytes: 4373368 num_examples: 455 download_size: 1896109 dataset_size: 4373368 - config_name: GeoQuestions1089_regression features: - name: question_id dtype: int64 - name: question dtype: string - name: answer list: float64 - name: answer_type list: string splits: - name: test num_bytes: 20755 num_examples: 231 download_size: 10620 dataset_size: 20755 - config_name: GeoSQA features: - name: question_id dtype: int64 - name: scenario_id dtype: int64 - name: answer dtype: string - name: annotation dtype: string - name: scenario dtype: string - name: question dtype: string - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string splits: - name: train num_bytes: 2350343 num_examples: 2644 - name: validation num_bytes: 566689 num_examples: 628 - name: test num_bytes: 762135 num_examples: 838 download_size: 1327080 dataset_size: 3679167 - config_name: GridRoute features: - name: matrix_size dtype: int64 - name: start list: int64 - name: end list: list: int64 - name: obstacles_coords list: list: int64 - name: path list: list: int64 splits: - name: test num_bytes: 439500 num_examples: 300 download_size: 16947 dataset_size: 439500 - config_name: MsMarco features: - name: question_id dtype: int64 - name: question dtype: string - name: answer dtype: string - name: passages list: - name: is_selected dtype: int64 - name: passage_text dtype: string - name: url dtype: string splits: - name: test num_bytes: 10860618 num_examples: 2907 - name: train num_bytes: 90739271 num_examples: 23513 - name: validation num_bytes: 16126312 num_examples: 4149 download_size: 58502647 dataset_size: 117726201 - config_name: NY-POI features: - name: long-term_check-ins list: list: string - name: recent_check-ins list: list: string - name: candidates list: list: string - name: ground_truth list: string splits: - name: test num_bytes: 9070607 num_examples: 1347 download_size: 3818269 dataset_size: 9070607 - config_name: PPNL_multi features: - name: matrix_size dtype: int64 - name: world_description dtype: string - name: world list: list: int64 - name: obstacles_coords list: list: int64 - name: start list: int64 - name: end list: list: int64 - name: n_goals dtype: int64 - name: path list: list: int64 - name: agent_as_a_point dtype: string - name: agent_has_direction dtype: string - name: distribution dtype: string splits: - name: test num_bytes: 80282702 num_examples: 55440 - name: train num_bytes: 76667038 num_examples: 53440 - name: validation num_bytes: 9587004 num_examples: 6680 download_size: 13201821 dataset_size: 166536744 - config_name: PPNL_single features: - name: matrix_size dtype: int64 - name: world_description dtype: string - name: world list: list: int64 - name: obstacles_coords list: list: int64 - name: start list: int64 - name: end list: list: int64 - name: n_goals dtype: int64 - name: path list: list: int64 - name: agent_as_a_point dtype: string - name: agent_has_direction dtype: string - name: distribution dtype: string splits: - name: test num_bytes: 15738553 num_examples: 19044 - name: train num_bytes: 12749254 num_examples: 16032 - name: validation num_bytes: 1594684 num_examples: 2004 download_size: 1341236 dataset_size: 30082491 - config_name: SpartUN features: - name: scenario_id dtype: string - name: question_id dtype: string - name: scenario dtype: string - name: question dtype: string - name: candidates_answers list: string - name: answer list: string - name: type dtype: string - name: k_hop dtype: int64 splits: - name: test num_bytes: 3597916 num_examples: 5551 - name: train num_bytes: 24431833 num_examples: 37095 - name: validation num_bytes: 3562581 num_examples: 5600 download_size: 3174385 dataset_size: 31592330 - config_name: SpatialEvalLLM features: - name: question dtype: string - name: answer dtype: string - name: struct_type dtype: string - name: size dtype: string - name: k_hop dtype: string - name: seed dtype: string - name: description_level dtype: string splits: - name: test num_bytes: 1091123 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 list: list: float64 - name: answers_names list: string - name: answers_adresses list: string - name: answers_sum_reviews list: string - name: answers_reviews list: list: string - name: answers_lat_longs list: list: float64 splits: - name: test num_bytes: 7601034 num_examples: 2173 - name: train num_bytes: 74876719 num_examples: 19960 - name: validation num_bytes: 7348256 num_examples: 2119 download_size: 45129970 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
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. ```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