---
configs:
- config_name: "10_shot_rlw"
  data_files:
  - split: dev
    path: "10_shot_rlw/dev.*"
  - split: ood_cons_count_10
    path: "10_shot_rlw/ood_cons_count_10.*"
  - split: ood_cons_count_3
    path: "10_shot_rlw/ood_cons_count_3.*"
  - split: ood_cons_count_5
    path: "10_shot_rlw/ood_cons_count_5.*"
  - split: ood_cons_count_7
    path: "10_shot_rlw/ood_cons_count_7.*"
  - split: ood_cons_len_10
    path: "10_shot_rlw/ood_cons_len_10.*"
  - split: ood_cons_len_3
    path: "10_shot_rlw/ood_cons_len_3.*"
  - split: ood_cons_len_5
    path: "10_shot_rlw/ood_cons_len_5.*"
  - split: ood_cons_len_7
    path: "10_shot_rlw/ood_cons_len_7.*"
  - split: ood_lexical
    path: "10_shot_rlw/ood_lexical.*"
  - split: test
    path: "10_shot_rlw/test.*"
  - split: train
    path: "10_shot_rlw/train.*"
- config_name: "1_shot_eng"
  data_files:
  - split: dev
    path: "1_shot_eng/dev.*"
  - split: ood_cons_count_3
    path: "1_shot_eng/ood_cons_count_3.*"
  - split: ood_cons_count_5
    path: "1_shot_eng/ood_cons_count_5.*"
  - split: ood_cons_len_3
    path: "1_shot_eng/ood_cons_len_3.*"
  - split: ood_cons_len_5
    path: "1_shot_eng/ood_cons_len_5.*"
  - split: ood_lexical
    path: "1_shot_eng/ood_lexical.*"
  - split: other_tasks_id
    path: "1_shot_eng/other_tasks_id.*"
  - split: other_tasks_ood
    path: "1_shot_eng/other_tasks_ood.*"
  - split: test
    path: "1_shot_eng/test.*"
  - split: train
    path: "1_shot_eng/train.*"
- config_name: "1_shot_rlw"
  data_files:
  - split: dev
    path: "1_shot_rlw/dev.*"
  - split: ood_cons_count_10
    path: "1_shot_rlw/ood_cons_count_10.*"
  - split: ood_cons_count_3
    path: "1_shot_rlw/ood_cons_count_3.*"
  - split: ood_cons_count_5
    path: "1_shot_rlw/ood_cons_count_5.*"
  - split: ood_cons_count_7
    path: "1_shot_rlw/ood_cons_count_7.*"
  - split: ood_cons_len_10
    path: "1_shot_rlw/ood_cons_len_10.*"
  - split: ood_cons_len_3
    path: "1_shot_rlw/ood_cons_len_3.*"
  - split: ood_cons_len_5
    path: "1_shot_rlw/ood_cons_len_5.*"
  - split: ood_cons_len_7
    path: "1_shot_rlw/ood_cons_len_7.*"
  - split: ood_lexical
    path: "1_shot_rlw/ood_lexical.*"
  - split: test
    path: "1_shot_rlw/test.*"
  - split: train
    path: "1_shot_rlw/train.*"
- config_name: "1_shot_rlw_10x"
  data_files:
  - split: dev
    path: "1_shot_rlw_10x/dev.*"
  - split: ood_cons_count_10
    path: "1_shot_rlw_10x/ood_cons_count_10.*"
  - split: ood_cons_count_3
    path: "1_shot_rlw_10x/ood_cons_count_3.*"
  - split: ood_cons_count_5
    path: "1_shot_rlw_10x/ood_cons_count_5.*"
  - split: ood_cons_count_7
    path: "1_shot_rlw_10x/ood_cons_count_7.*"
  - split: ood_cons_len_10
    path: "1_shot_rlw_10x/ood_cons_len_10.*"
  - split: ood_cons_len_3
    path: "1_shot_rlw_10x/ood_cons_len_3.*"
  - split: ood_cons_len_5
    path: "1_shot_rlw_10x/ood_cons_len_5.*"
  - split: ood_cons_len_7
    path: "1_shot_rlw_10x/ood_cons_len_7.*"
  - split: ood_lexical
    path: "1_shot_rlw_10x/ood_lexical.*"
  - split: test
    path: "1_shot_rlw_10x/test.*"
  - split: train
    path: "1_shot_rlw_10x/train.*"
- config_name: "2_shot_rlw"
  data_files:
  - split: dev
    path: "2_shot_rlw/dev.*"
  - split: ood_cons_count_10
    path: "2_shot_rlw/ood_cons_count_10.*"
  - split: ood_cons_count_3
    path: "2_shot_rlw/ood_cons_count_3.*"
  - split: ood_cons_count_5
    path: "2_shot_rlw/ood_cons_count_5.*"
  - split: ood_cons_count_7
    path: "2_shot_rlw/ood_cons_count_7.*"
  - split: ood_cons_len_10
    path: "2_shot_rlw/ood_cons_len_10.*"
  - split: ood_cons_len_3
    path: "2_shot_rlw/ood_cons_len_3.*"
  - split: ood_cons_len_5
    path: "2_shot_rlw/ood_cons_len_5.*"
  - split: ood_cons_len_7
    path: "2_shot_rlw/ood_cons_len_7.*"
  - split: ood_lexical
    path: "2_shot_rlw/ood_lexical.*"
  - split: test
    path: "2_shot_rlw/test.*"
  - split: train
    path: "2_shot_rlw/train.*"
- config_name: "3_shot_rlw"
  data_files:
  - split: dev
    path: "3_shot_rlw/dev.*"
  - split: ood_cons_count_10
    path: "3_shot_rlw/ood_cons_count_10.*"
  - split: ood_cons_count_3
    path: "3_shot_rlw/ood_cons_count_3.*"
  - split: ood_cons_count_5
    path: "3_shot_rlw/ood_cons_count_5.*"
  - split: ood_cons_count_7
    path: "3_shot_rlw/ood_cons_count_7.*"
  - split: ood_cons_len_10
    path: "3_shot_rlw/ood_cons_len_10.*"
  - split: ood_cons_len_3
    path: "3_shot_rlw/ood_cons_len_3.*"
  - split: ood_cons_len_5
    path: "3_shot_rlw/ood_cons_len_5.*"
  - split: ood_cons_len_7
    path: "3_shot_rlw/ood_cons_len_7.*"
  - split: ood_lexical
    path: "3_shot_rlw/ood_lexical.*"
  - split: test
    path: "3_shot_rlw/test.*"
  - split: train
    path: "3_shot_rlw/train.*"
- config_name: "5_shot_rlw"
  data_files:
  - split: dev
    path: "5_shot_rlw/dev.*"
  - split: ood_cons_count_10
    path: "5_shot_rlw/ood_cons_count_10.*"
  - split: ood_cons_count_3
    path: "5_shot_rlw/ood_cons_count_3.*"
  - split: ood_cons_count_5
    path: "5_shot_rlw/ood_cons_count_5.*"
  - split: ood_cons_count_7
    path: "5_shot_rlw/ood_cons_count_7.*"
  - split: ood_cons_len_10
    path: "5_shot_rlw/ood_cons_len_10.*"
  - split: ood_cons_len_3
    path: "5_shot_rlw/ood_cons_len_3.*"
  - split: ood_cons_len_5
    path: "5_shot_rlw/ood_cons_len_5.*"
  - split: ood_cons_len_7
    path: "5_shot_rlw/ood_cons_len_7.*"
  - split: ood_lexical
    path: "5_shot_rlw/ood_lexical.*"
  - split: test
    path: "5_shot_rlw/test.*"
  - split: train
    path: "5_shot_rlw/train.*"

annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- other
multilinguality:
- monolingual
pretty_name: Templatic Generation Tasks for In-Context Learning Research
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
tags:
- seq2seq
task_categories:
- text2text-generation
task_ids: []
---
# Dataset Card for Active/Passive/Logical Transforms

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Dataset Subsets (Tasks)](#data-tasks)
  - [Dataset Splits](#data-splits)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:**
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** [Roland Fernandez](mailto:rfernand@microsoft.com)

### Dataset Summary

This dataset is a synthetic dataset containing a set of templatic generation tasks using both English and random 2-letter words. 

### Supported Tasks and Leaderboards

[TBD]

### Languages

All data is in English or random 2-letter words.  

## Dataset Structure

The dataset consists of several subsets, or tasks. Each task contains a train split, a dev split, and a
test split, and multiple out-of-distribution splits.

Each sample in a split contains a source string, a target string, and an annotation string (describing the sample).

### Dataset Subsets (Tasks)
The dataset consists of the following tasks:

```
- 1_shot_rlw              (1 example input/output pair, a test input, and the gold output, all using random 2-letter words)
- 1_shot_eng              (same as 1_shot_rlw but using English words).
- 1_shot_rlw_10x          (same as 1_shot_rlw, but with 10x the training samples) 
- 2_shot_rlw              (2 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
- 3_shot_rlw              (3 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
- 5_shot_rlw              (5 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
- 10_shot_rtw             (10 example input/output pairs, a test input, and the gold output, all using random 2-letter words)
```

### Data Splits

Most tasks have the following splits:
  - train
  - dev
  - test
  - ood_lexical
  - ood_cons_count_3
  - ood_cons_count_5
  - ood_cons_count_7
  - ood_cons_count_10
  - ood_cons_len_3
  - ood_cons_len_5
  - ood_cons_len_7
  - ood_cons_len_10
  
Here is a table showing how the number of examples varies by split (for most tasks):

| Dataset Split | Number of Instances in Split                |
| ------------- | ------------------------------------------- |
| train         | 280,000                                     |
| dev           |  35,000                                     |
| test          |  35,000                                     |
| ood_*         |  84,000                                     |


### Data Instances

Each sample consits of a source, target, and annotation string (all tab separated).

Here is an example from the *train* split of the *1_shot_eng* task:

```
{
  'raw': 'Q any mouse ) ; bear A any mouse & . Q road ) ; building A	road & .	{"cons_count": "Q2A1", "cons_len": "Q21.Q11"}'

  'source': 'Q any mouse ) ; bear A any mouse & . Q road ) ; building A',
  'target': 'road & .',
  'annotation': '{"cons_count": "Q2A1", "cons_len": "Q21.Q11"}'
}
```

### Data Fields

- `source`: the string containing the N-shot examples and the test cue
- `target`: the string containing the desired (gold) output
- `annotation`: the string describing the example (as a python or JSON dictionary)  

## Dataset Creation

### Curation Rationale

We wanted a dataset that would test in-context (and from scratch) learning of abstract, semantic-free symbolic transformations, 
based on a random template for each example.  The dataset is designed to test 3 types of out of distribution generalization:

  - lexical - known words used in new contexts (relative to train split)
  - length - train split uses constituents of 1, 2, or 4 words; OOD splits use 3, 5, 7, or 10 words
  - count - train split uses 1, 2, or 4 constituents; OOD splits use 3, 5, 7, or 10 constituents

### Source Data

[N/A]

#### Initial Data Collection and Normalization

[N/A]

#### Who are the source language producers?

The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.

### Annotations

Besides the source and target strings, each sample contains an annotation string that describes the sample.

#### Annotation process

The annotation columns were generated from each sample template.

#### Who are the annotators?

[N/A]

### Personal and Sensitive Information

No names or other sensitive information are included in the data.

## Considerations for Using the Data

### Social Impact of Dataset

The purpose of this dataset is to research how LLM and from-scratch model can learn to solve templatic generation tasks.  

### Discussion of Biases

[TBD]

### Other Known Limitations

[TBD]

## Additional Information

The internal name of this dataset is nc_tgt_v11.  Also see DATASET_INFO.md and GRAMMAR.md files.

### Dataset Curators

The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.

### Licensing Information

This dataset is released under the [Permissive 2.0 license](https://cdla.dev/permissive-2-0/). 

### Citation Information

[TBD]

### Contributions

Thanks to [The Neurocompositional AI group at Microsoft Research](https://www.microsoft.com/en-us/research/project/neurocompositional-ai/) for creating and adding this dataset.