--- 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: StepGame data_files: - split: train path: StepGame/train-* - split: validation path: StepGame/validation-* - split: test path: StepGame/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: 20649 num_examples: 231 download_size: 10655 dataset_size: 20649 - 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: answer list: string splits: - name: test num_bytes: 9088765 num_examples: 1347 download_size: 3829714 dataset_size: 9088765 - 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 list: string - name: scenario 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: 1100727 num_examples: 1400 download_size: 203297 dataset_size: 1100727 - config_name: StepGame features: - name: scenario dtype: string - name: question dtype: string - name: answer dtype: string - name: k_hop dtype: int64 - name: candidates_answers list: string splits: - name: train num_bytes: 15871676 num_examples: 50000 - name: validation num_bytes: 1584479 num_examples: 5000 - name: test num_bytes: 65697697 num_examples: 100000 download_size: 18189276 dataset_size: 83153852 - 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: 7590110 num_examples: 2153 - name: train num_bytes: 74777277 num_examples: 19762 - name: validation num_bytes: 7343485 num_examples: 2109 download_size: 45062284 dataset_size: 89710872 --- # Dataset Card for GeoBenchLLM ## Table of Contents ## Dataset Description - **Homepage:** https://github.com/Rfr2003/GeoBenchLLM - **Repository:** https://github.com/Rfr2003/GeoBenchLLM - **Paper:** - **Point of Contact:** rodrigo.ferreira-rodrigues@utoulouse.fr ### Dataset Summary GeoBenchLLM 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/GeoBenchLLM", "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 Please report to the dataset viewer to see what an instance for each dataset looks like. ### Data Fields We will give for each dataset the data fields. Note that fields highlighted by 🟦 are required to formulate the question and fields highlighted by 🟩 contain the answer to the question. Every other fields can either be used to perform some analytics or to formulate differents tasks on the same dataset. - **GeoQuestions1089_coord**: - 🟦 `question`(`str`) : the question to be answered. - 🟩 `answer`(`List[float]`) : the coordinates of the answer. The first element of the list correspond to the latitude and the second to the longitude. - **GeoQuestions1089_YN**: - 🟦 `question`(`str`) : the question to be answered. - 🟩 `answer`(`List[bool]`) : a list containing the boolean corresponding to the answer. - **GeoQuestions1089_regression** and **GeoQuery_regression**: - 🟦 `question`(`str`) : the question to be answered. - 🟩 `answer`(`List[float]`) : a list containing the numbers to be predicted. - **GeoQuestions1089_place** and **GeoQuery_place**: - 🟦 `question`(`str`) : the question to be answered. - 🟩 `answer`(`List[str]`) : a list containing the names of the places to be predicted. - **Ms-Marco_place**: - 🟦 `question`(`str`) : the question to be answered. - 🟩 `answer`(`str`) : the answer to the question formulated by a human. - `question_id`(`int64`) : the id of the question from the original dataset. - `passages`(`List[dict]`) : a list of dicts. Each dict correspond to a passage and gives the following information: - `is_selected`(`int64`) : 1 if the passage was selected to write the answer, 0 otherwise. - `passage_text`(`str`) : the text of the passage. - `url`(`str`) : the url from where the passage was retrieved. - **GeoSQA**: - 🟦 `annotation`(`str`) : the description of the image normally used to answer the question. - 🟦 `scenario`(`str`) : the scenario attached to the image providing context to the question. - 🟦 `question`(`str`) : the question to be answered. - 🟦 `A`(`str`) : one of the possibles answers to the question. - 🟦 `B`(`str`) : one of the possibles answers to the question. - 🟦 `C`(`str`) : one of the possibles answers to the question. - 🟦 `D`(`str`) : one of the possibles answers to the question. - 🟩 `answer`(`str`) : the letter corresponding to the right choice. - `question_id`(`int64`) : the id of the question from the original dataset. - `scenario_id`(`int64`) : the id of the scenario from the original dataset. - **GKMC**: - 🟦 `scenario`(`str`) : the scenario providing context to the question. - 🟦 `question`(`str`) : the question to be answered. - 🟦 `A`(`str`) : one of the possibles answers to the question. - 🟦 `B`(`str`) : one of the possibles answers to the question. - 🟦 `C`(`str`) : one of the possibles answers to the question. - 🟦 `D`(`str`) : one of the possibles answers to the question. - 🟩 `answer`(`str`) : the letter corresponding to the right choice. - `question_id`(`int64`) : the id of the question from the original dataset. - **SpatialEvalLLM**: - 🟦 `scenario`(`str`) : the scenario providing context to the question. - 🟦 `question`(`str`) : the question to be answered. - 🟩 `answer`(`List[str]`) : a list containing the names of the right objects to predict. - `struct_type`(`str`) : the geometric structure of the map. - `size`(`str`) : the size of the structure in number of tiles composing it. - `k_hop`(`str`) : the minimum reasoning steps required to answer the question. - `seed`(`str`) : the seed used to generate the question. - `description_level`(`str`) : if **global** then the entierity of the map is described. If **local**, only a portion of the map is described. - **SpartUN**: - 🟦 `scenario`(`str`) : the scenario providing context to the question. - 🟦 `question`(`str`) : the question to be answered. - 🟦 `candidates_answers`(`List[str]`) : the candidates answers from which the model has to retrieve. - 🟩 `answer`(`List[str]`) : a list containing the right answers from the candidate list. - `question_id`(`str`) : the id of the question from the original dataset. - `scenario_id`(`str`) : the id of the scenario from the original dataset. - `type`(`str`) : **YN** from boolean questions, **FR** for Find Relation questions. - `k_hop`(`int64`) : the minimum reasoning steps required to answer the question. - **StepGame**: - 🟦 `scenario`(`str`) : the scenario providing context to the question. - 🟦 `question`(`str`) : the question to be answered. - 🟦 `candidates_answers`(`List[str]`) : the candidates answers from which the model has to retrieve. - 🟩 `answer`(`List[str]`) : a list containing the right answers from the candidate list. - `k_hop`(`int64`) : the minimum reasoning steps required to answer the question. - **TourismQA**: - 🟦 `question`(`str`) : the question to be answered. - 🟩 `answers_names`(`List[str]`) : a list containing the names of the POI to be recommended (answer expected). - `city`(`dict`) : a dict containing the following informations about the city where take place the question: - `coord`(`List[float]`) : the coordinates of the city. The first element of the list correspond to the latitude and the second to the longitude. - `name`(`str`) : the name of the city. - `tagged_locations`(`List[str]`) : the locations names retrieved from the question (not used for our description of the task). - `tagged_locations_lat_long`(`List[flaot]`) : the latitudes and longitudes of the locations retrieved from the question (not used for our description of the task). - `answers_adresses`(`List[str]`) : the postal adresses of each answer (not used for our description of the task). - `answers_reviews`(`List[List[str]]`) : for each POI, we have a list of reviews (not used for our description of the task). - `answers_sum_reviews`(`List[str]`) : a summarization of the reviews for each POI retrieved from ??? work (not used for our description of the task). - `answers_lat_longs`(`List[str]`) : the latitudes and longitudes of the answers (not used for our description of the task). - **NY-POI**: - 🟦 `long-term_check-ins`(`List[List[str]]`) : a list of long-term check-ins from the same user. Each check-in is list composed in the order : POI id, POI category and time of visit in UTC. - 🟦 `recent_check-ins`(`List[List[str]]`) : a list of recent check-ins from an user. Each check-in is list composed in the order : POI id, POI category and time of visit in UTC. - 🟦 `candidates`(`List[List[str]]`) : a list of POI candidates containing the answer. For each POI, we have its id, its distance from the last visited POI in the recent check-ins list and its category. - 🟩 `answer`(`List[str]`) : the id of the POI corresponding to the answer. - **GridRoute**: - 🟦 `matrix_size`(`int64`) : the size of the squared matrix. - 🟦 `start`(`List[int64]`) : the coordinates of the starting point. The first element of the list is the x coordinate and the second is the y one. - 🟦 `end`(`List[List[int64]]`) : a list of ending points that the has to reach. This dataset only has one end point per question. - 🟦 `obstacles_coords`(`List[List[int64]]`) : a list of coordinates corresponding to the obstacles that we have to avoid. For each point, the first element of the list is the x coordinate and the second is the y one. - 🟩 `path`(`List[List[int64]]`) : a list of coordinates corresponding to the optimal path. For each point, the first element of the list is the x coordinate and the second is the y one. - **PPNL_single**: - 🟦 `matrix_size`(`int64`) : the size of the squared matrix. - 🟦 `start`(`List[int64]`) : the coordinates of the starting point. The first element of the list is the x coordinate and the second is the y one. - 🟦 `end`(`List[List[int64]]`) : a list of ending points that the has to reach. This dataset only has one end point per question. - 🟦 `obstacles_coords`(`List[List[int64]]`) : a list of coordinates corresponding to the obstacles that we have to avoid. For each point, the first element of the list is the x coordinate and the second is the y one. - 🟩 `path`(`List[List[int64]]`) : a list of coordinates corresponding to the optimal path. For each point, the first element of the list is the x coordinate and the second is the y one. If there is no path possible, this field is an empty list. - `world_description`(`str`) : a description of the world in natural language. Can be used to directly prompt the model. - `n_goals`(`int64`) : the number of end points to reach. - `agent_as_a_point`(`str`) : the solution path described as if the model is a point. - `agent_has_direction`(`str`) : the solution path described as directions. - `distribution`(`str`) : **iid** if the example has the same properties (matrix size, initial location/goal placements and number of obstacles), **ood** otherwise. - **PPNL_multi**: - 🟦 `matrix_size`(`int64`) : the size of the squared matrix. - 🟦 `start`(`List[int64]`) : the coordinates of the starting point. The first element of the list is the x coordinate and the second is the y one. - 🟦 `end`(`List[List[int64]]`) : a list of ending points that the has to reach. Each question has at least 2 goals to reach. - 🟦 `obstacles_coords`(`List[List[int64]]`) : a list of coordinates corresponding to the obstacles that we have to avoid. For each point, the first element of the list is the x coordinate and the second is the y one. - 🟩 `path`(`List[List[int64]]`) : a list of coordinates corresponding to the optimal path. For each point, the first element of the list is the x coordinate and the second is the y one. If there is no path possible, this field is an empty list. - `world_description`(`str`) : a description of the world in natural language. Can be used to directly prompt the model. - `n_goals`(`int64`) : the number of end points to reach. - `agent_as_a_point`(`str`) : the solution path described as if the model is a point. - `agent_has_direction`(`str`) : the solution path described as directions. - `distribution`(`str`) : **iid** if the example has the same properties (matrix size, initial location/goal placements and number of obstacles), **ood** otherwise. ### Data Splits | Cogn. Level | Tasks | Datasets | Train | Dev | Test | | --------------- | ---------------------- | ---------------------------------------- | --------------------- | ------------------- | ------------------------- | | **Knowledge** | Coordinates Prediction | GeoQuestions1089_coord | – | – | 87 | | | Yes/No questions | GeoQuestions1089_YN | – | – | 181 | | | Regression | GeoQuestions1089_regression
GeoQuery_regression | –
182 | –
17 | 231
89 | | | Place prediction | GeoQuestions1089_place
GeoQuery_place
MS-Marco_place | –
346
23 513 | –
33
4 149 | 455
184
2 907 | | **──────────** | **──────────** | **──────────** | **──────────** | **──────────** | **──────────** | | **Reasoning** | Scenario Complex QA | GeoSQA
GKMC | 2 644
– | 628
– | 838
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 | 19 762
– | 2 109
– | 2 153
1 347 | | | Path Finding | GridRoute
PPNL_single
PPNL_multi | –
16 032
53 440 | –
2 004
6 680 | 300
19 044
55 440 | | **──────────** | **──────────** | **──────────** | **──────────** | **──────────** | **──────────** | | **Total** | – | – | **203 014** | **26 220** | **191 807** | ## 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. 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