README.md

---
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: symbolic_reasoning
    dtype: 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: 1.1.0
    data_files:
      - split: train
        path: "sparp/SpaRP-PS1 (SpaRTUN)/train.json"
      - split: validation
        path: "sparp/SpaRP-PS1 (SpaRTUN)/val.json"
      - split: test
        path: "sparp/SpaRP-PS1 (SpaRTUN)/test.json"

  - config_name: "SpaRP-PS2 (StepGame)"
    version: 1.1.0
    data_files:
      - split: train
        path: "sparp/SpaRP (StepGame)/PS2/train.json"
      - split: validation
        path: "sparp/SpaRP (StepGame)/PS2/val.json"
      - split: test
        path: "sparp/SpaRP (StepGame)/PS2/test.json"

  - config_name: "SpaRP-PS3 (StepGame-Ext-01)"
    version: 1.1.0
    data_files:
      - split: train
        path: "sparp/SpaRP (StepGame)/PS3/train.json"
      - split: validation
        path: "sparp/SpaRP (StepGame)/PS3/val.json"
      - split: test
        path: "sparp/SpaRP (StepGame)/PS3/test.json"
  
  - config_name: "SpaRP-PS4 (StepGame-Ext-02)"
    version: 1.1.0
    data_files:
      - split: train
        path: "sparp/SpaRP (StepGame)/PS4/train.json"
      - split: validation
        path: "sparp/SpaRP (StepGame)/PS4/val.json"
      - split: test
        path: "sparp/SpaRP (StepGame)/PS4/test.json"

  - config_name: "small-SpaRP-PS1 (SpaRTUN)"
    version: 1.1.0
    data_files:
      - split: train
        path: "small-sparp/SpaRP-PS1 (SpaRTUN)/train.json"
      - split: validation
        path: "small-sparp/SpaRP-PS1 (SpaRTUN)/val.json"
      - split: test
        path: "small-sparp/SpaRP-PS1 (SpaRTUN)/test.json"

  - config_name: "small-SpaRP-PS2 (StepGame)"
    version: 1.1.0
    data_files:
      - split: train
        path: "small-sparp/SpaRP (StepGame)/PS2/train.json"
      - split: validation
        path: "small-sparp/SpaRP (StepGame)/PS2/val.json"
      - split: test
        path: "small-sparp/SpaRP (StepGame)/PS2/test.json"

  - config_name: "small-SpaRP-PS3 (StepGame-Ext-01)"
    version: 1.1.0
    data_files:
      - split: train
        path: "small-sparp/SpaRP (StepGame)/PS3/train.json"
      - split: validation
        path: "small-sparp/SpaRP (StepGame)/PS3/val.json"
      - split: test
        path: "small-sparp/SpaRP (StepGame)/PS3/test.json"
  
  - config_name: "small-SpaRP-PS4 (StepGame-Ext-02)"
    version: 1.1.0
    data_files:
      - split: train
        path: "small-sparp/SpaRP (StepGame)/PS4/train.json"
      - split: validation
        path: "small-sparp/SpaRP (StepGame)/PS4/val.json"
      - split: test
        path: "small-sparp/SpaRP (StepGame)/PS4/test.json"

  # - config_name: SpartQA_Human
  #   version: 1.1.0
  #   data_files:
  #     - split: train
  #       path: "SpartQA_Human/train.json"
  #     - split: validation
  #       path: "SpartQA_Human/val.json"
  #     - split: test
  #       path: "SpartQA_Human/test.json"
  # - config_name: ReSQ
  #   version: 1.1.0
  #   data_files:
  #     - split: train
  #       path: "ReSQ/train.json"
  #     - split: validation
  #       path: "ReSQ/val.json"
  #     - split: test
  #       path: "ReSQ/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](). 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:

- *context*: Textual description of the spatial context.
- *question*: A question about finding spatial relations between two entities in the context.
- *targets*: Answer i.e. list of spatial relations between the entities in the question.
- *target_choices*: List of all the spatial relations to choose from.
- *target_scores*: Binarized Multi-label representation of targets over target_choices.
- *reasoning*: Verbalized reasoning path as deductively-verified CoT for training or few-shot examples.

Additionally, the fields with the metadata are:

- *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. 
- *question_id*: An identifier from the source data corresponding to the question.
- *num_hop*: Ground truth number of hop required for the question.
- *symbolic_context*: A json string describing the symbolic context.
- *symbolic_entity_map*: A json string that maps symbolic entities to their complete descriptive names used.
- *symbolic_question*: A list containing head and tail entities of the question.
- *symbolic_reasoning*: A json string containing symbolic reasoning steps.
- *num_context_entities*: Number of entities in the context.
- *num_question_entities*: Number of entities in the question.
- *question_type*: Type of the question. Only `FR` i.e. Find Relation type questions are currently present.
- *canary*: A canary string present only in the `test`.
- *reasoning_types*: The type of reasoning copied from the source data required for answering the question.
- *spatial_types*: The type of spatial relations copied from the source data required for answering the question.
- *source_data*: The upstream source of the data (either SpaRTUN or StepGame) for a given instance.
- *comments*: Additional comments specific to the upstream data.

### 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 = "",
}
```