Datasets:

Modalities:
Tabular
Text
Formats:
json
Libraries:
Datasets
pandas
License:
File size: 3,766 Bytes
70b0865
8e7543e
 
70b0865
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8e7543e
70b0865
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8e7543e
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
70b0865
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7a733b2
70b0865
 
 
 
 
f20b415
70b0865
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
---
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 = "",
}
```