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--- |
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license: cc-by-sa-4.0 |
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dataset_info: |
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features: |
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- name: instruction |
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dtype: string |
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- name: context |
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dtype: string |
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- name: question |
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dtype: string |
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- name: targets |
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sequence: string |
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- name: target_choices |
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sequence: string |
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- name: target_scores |
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sequence: int32 |
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- name: reasoning |
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dtype: string |
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- name: source_data |
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dtype: string |
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- name: context_id |
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dtype: int32 |
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- name: question_id |
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dtype: int32 |
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- name: symbolic_context |
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dtype: string |
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- name: symbolic_entity_map |
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dtype: string |
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- name: symbolic_question |
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sequence: string |
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- name: symbolic_reasoning |
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dtype: string |
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- name: num_context_entities |
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dtype: int32 |
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- name: num_question_entities |
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dtype: int32 |
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- name: question_type |
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dtype: string |
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- name: reasoning_types |
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sequence: string |
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- name: spatial_types |
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sequence: string |
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- name: commonsense_question |
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dtype: string |
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- name: canary |
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dtype: string |
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- name: comments |
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sequence: string |
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configs: |
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- config_name: "SpaRP-PS1 (SpaRTUN)" |
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version: 1.1.0 |
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data_files: |
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- split: train |
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path: "sparp/SpaRP-PS1 (SpaRTUN)/train.json" |
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- split: validation |
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path: "sparp/SpaRP-PS1 (SpaRTUN)/val.json" |
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- split: test |
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path: "sparp/SpaRP-PS1 (SpaRTUN)/test.json" |
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- config_name: "SpaRP-PS2 (StepGame)" |
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version: 1.1.0 |
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data_files: |
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- split: train |
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path: "sparp/SpaRP (StepGame)/PS2/train.json" |
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- split: validation |
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path: "sparp/SpaRP (StepGame)/PS2/val.json" |
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- split: test |
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path: "sparp/SpaRP (StepGame)/PS2/test.json" |
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- config_name: "SpaRP-PS3 (StepGame-Ext-01)" |
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version: 1.1.0 |
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data_files: |
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- split: train |
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path: "sparp/SpaRP (StepGame)/PS3/train.json" |
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- split: validation |
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path: "sparp/SpaRP (StepGame)/PS3/val.json" |
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- split: test |
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path: "sparp/SpaRP (StepGame)/PS3/test.json" |
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- config_name: "SpaRP-PS4 (StepGame-Ext-02)" |
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version: 1.1.0 |
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data_files: |
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- split: train |
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path: "sparp/SpaRP (StepGame)/PS4/train.json" |
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- split: validation |
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path: "sparp/SpaRP (StepGame)/PS4/val.json" |
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- split: test |
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path: "sparp/SpaRP (StepGame)/PS4/test.json" |
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- config_name: "small-SpaRP-PS1 (SpaRTUN)" |
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version: 1.1.0 |
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data_files: |
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- split: train |
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path: "small-sparp/SpaRP-PS1 (SpaRTUN)/train.json" |
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- split: validation |
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path: "small-sparp/SpaRP-PS1 (SpaRTUN)/validation.json" |
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- split: test |
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path: "small-sparp/SpaRP-PS1 (SpaRTUN)/test.json" |
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- config_name: "small-SpaRP-PS2 (StepGame)" |
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version: 1.1.0 |
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data_files: |
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- split: train |
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path: "small-sparp/SpaRP (StepGame)/PS2/train.json" |
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- split: validation |
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path: "small-sparp/SpaRP (StepGame)/PS2/validation.json" |
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- split: test |
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path: "small-sparp/SpaRP (StepGame)/PS2/test.json" |
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|
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- config_name: "small-SpaRP-PS3 (StepGame-Ext-01)" |
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version: 1.1.0 |
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data_files: |
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- split: train |
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path: "small-sparp/SpaRP (StepGame)/PS3/train.json" |
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- split: validation |
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path: "small-sparp/SpaRP (StepGame)/PS3/validation.json" |
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- split: test |
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path: "small-sparp/SpaRP (StepGame)/PS3/test.json" |
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- config_name: "small-SpaRP-PS4 (StepGame-Ext-02)" |
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version: 1.1.0 |
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data_files: |
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- split: train |
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path: "small-sparp/SpaRP (StepGame)/PS4/train.json" |
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- split: validation |
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path: "small-sparp/SpaRP (StepGame)/PS4/validation.json" |
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- split: test |
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path: "small-sparp/SpaRP (StepGame)/PS4/test.json" |
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--- |
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# Dataset Card for Spatial Reasoning Path (SpaRP) |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Languages](#languages) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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## Dataset Description |
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- **Repository: https://github.com/UKPLab/acl2024-sparc-and-sparp** |
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- **Paper: https://arxiv.org/abs/** |
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- **Point of Contact: Md Imbesat Hassan Rizvi (http://www.ukp.tu-darmstadt.de/)** |
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### Dataset Summary |
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This dataset is a consolidation of SpaRTUN and StepGame datasets with an extension of additional spatial characterization and reasoning path generation. The methodology is explained in our ACL 2024 paper - [SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models](). The dataset format and fields are normalized across the two upstream benchmark datasets -- SpaRTUN and StepGame. The datasets are primarily a Spatial Question Answering datasets, which are enriched with verbalized reasoning paths. The prominent fields of interests are: |
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- *context*: Textual description of the spatial context. |
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- *question*: A question about finding spatial relations between two entities in the context. |
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- *targets*: Answer i.e. list of spatial relations between the entities in the question. |
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- *target_choices*: List of all the spatial relations to choose from. |
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- *target_scores*: Binarized Multi-label representation of targets over target_choices. |
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- *reasoning*: Verbalized reasoning path as deductively-verified CoT for training or few-shot examples. |
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Additionally, the fields with the metadata are: |
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- *context_id*: An identifier from the source data corresponding to the context. `context_id` is unique. A single context can have multiple questions (e.g. SpaRTUN). Hence, (context_id, question_id) is a unique identifier for a dataset instance. |
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- *question_id*: An identifier from the source data corresponding to the question. |
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- *num_hop*: Ground truth number of hop required for the question. |
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- *symbolic_context*: A json string describing the symbolic context. |
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- *symbolic_entity_map*: A json string that maps symbolic entities to their complete descriptive names used. |
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- *symbolic_question*: A list containing head and tail entities of the question. |
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- *symbolic_reasoning*: A json string containing symbolic reasoning steps. |
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- *num_context_entities*: Number of entities in the context. |
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- *num_question_entities*: Number of entities in the question. |
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- *question_type*: Type of the question. Only `FR` i.e. Find Relation type questions are currently present. |
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- *canary*: A canary string present only in the `test`. |
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- *reasoning_types*: The type of reasoning copied from the source data required for answering the question. |
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- *spatial_types*: The type of spatial relations copied from the source data required for answering the question. |
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- *source_data*: The upstream source of the data (either SpaRTUN or StepGame) for a given instance. |
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- *comments*: Additional comments specific to the upstream data. |
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### Languages |
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English |
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## Additional Information |
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You can download the data via: |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset("UKPLab/sparp") # default config is "SpaRP-PS1 (SpaRTUN)" |
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dataset = load_dataset("UKPLab/sparp", "SpaRP-PS2 (StepGame)") # use the "SpaRP-PS2 (StepGame)" tag for the StepGame dataset |
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``` |
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Please find more information about the code and how the data was collected on [GitHub](https://github.com/UKPLab/acl2024-sparc-and-sparp). |
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### Dataset Curators |
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Curation is managed by our [data manager](https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4235) at UKP. |
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### Licensing Information |
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[CC-by-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) |
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### Citation Information |
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Please cite this data using: |
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``` |
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@inproceedings{rizvi-2024-sparc, |
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title={SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models}, |
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author={Rizvi, Md Imbesat Hassan Rizvi and Zhu, Xiaodan and Gurevych, Iryna}, |
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editor = "", |
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booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics", |
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month = aug, |
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year = "2024", |
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address = "Bangkok, Thailand", |
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publisher = "Association for Computational Linguistics", |
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url = "", |
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doi = "", |
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pages = "", |
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} |
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``` |
