|
|
--- |
|
|
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. |
|
|
|
|
|
|
