| | ---
|
| | 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:
|
| | - mit
|
| | 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.
|
| |
|
| | ### 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.
|
| |
|
| | |
