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---
language:
- en
license: cc-by-sa-4.0
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: targets
sequence: string
- name: target_choices
sequence: string
- name: target_scores
sequence: int32
- name: reasoning
dtype: string
- name: source_data
dtype: string
- name: context_id
dtype: int32
- name: question_id
dtype: int32
- name: symbolic_context
dtype: string
- name: symbolic_entity_map
dtype: string
- name: symbolic_question
sequence: string
- name: num_context_entities
dtype: int32
- name: num_question_entities
dtype: int32
- name: question_type
dtype: string
- name: reasoning_types
sequence: string
- name: spatial_types
sequence: string
- name: commonsense_question
dtype: string
- name: canary
dtype: string
- name: comments
sequence: string
configs:
- config_name: SpaRP-PS1 (SpaRTUN)
version: 0.1.0
default: true
data_files:
- split: train
path: SpaRP-PS1 (SpaRTUN)/train.json
- split: validation
path: SpaRP-PS1 (SpaRTUN)/val.json
- split: test
path: SpaRP-PS1 (SpaRTUN)/test.json
- config_name: SpaRP-PS2 (StepGame)
version: 0.1.0
data_files:
- split: train
path: SpaRP (StepGame)/PS2/train.json
- split: validation
path: SpaRP (StepGame)/PS2/val.json
- split: test
path: SpaRP (StepGame)/PS2/test.json
---
# Dataset Card for Spatial Reasoning Path (SpaRP)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository: https://github.com/UKPLab/acl2024-sparc-and-sparp**
- **Paper: https://arxiv.org/abs/**
- **Point of Contact: Md Imbesat Hassan Rizvi (http://www.ukp.tu-darmstadt.de/)**
### Dataset Summary
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]().
### Languages
English
## Additional Information
You can download the data via:
```
from datasets import load_dataset
dataset = load_dataset("UKPLab/sparp") # default config is "SpaRP-PS1 (SpaRTUN)"
dataset = load_dataset("UKPLab/sparp", "SpaRP-PS2 (StepGame)") # use the "SpaRP-PS2 (StepGame)" tag for the StepGame dataset
```
Please find more information about the code and how the data was collected on [GitHub](https://github.com/UKPLab/acl2024-sparc-and-sparp).
### Dataset Curators
Curation is managed by our [data manager](https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4235) at UKP.
### Licensing Information
[CC-by-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
Please cite this data using:
```
@inproceedings{rizvi-2024-sparc,
title={SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language Models},
author={Rizvi, Md Imbesat Hassan Rizvi and Zhu, Xiaodan and Gurevych, Iryna},
editor = "",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "",
doi = "",
pages = "",
}
``` |