Dataset Card for ctga-v1

Dataset Details

ctga-v1 or conditional task generation with attributes is a new dataset created by remixing existing instruction tuning datasets (P3) to train Bonito.

from datasets import load_dataset
dataset = load_dataset("BatsResearch/ctga-v1")

Dataset Creation

The dataset is derived from P3 by annotating 323 prompt templates from 39 datasets with 16 task types.

The prompt templates in P3 are remixed to create the meta-templates, which, in turn, generate the training examples.

The meta-template input has a task type (<|tasktype|>) as an attribute followed by the unannotated text or context (<|context|>).

The output of the meta-template comprises the attributed task with the prompt or task description and the context ({context}) followed by a pipe symbol (<|pipe|>) and the solution to the task.

We use the <|pipe|> symbol to separate the instruction and response pair that is used for adapting the downstream model.

Data Instances

Each data instance contains the following features: context, task_input task_output dataset dataset_config task_type input and output.

The (input, output) is the pair we used to train Bonito model.

Data Fields

• 'context': input context
• 'task_input': prompted input without context
• 'task_output': corrosponding output
• 'dataset': source dataset
• 'dataset_config': source dataset configuration
• 'task_type': corrsponding task type
• 'input': reformatted input
• 'output': reformatted output

Source Data

All the datasets are sourced from the datasets library.

• Extractive Question Answering & Question Generation

  • adversarial_qa/dbert
  • adversarial_qa/dbidaf
  • adversarial_qa/droberta
  • duorc/ParaphraseRC
  • duorc/SelfRC
  • squad

• Topic Classification

  • ag_news
  • dbpedia_14
  • hellaswag
  • duorc/ParaphraseRC
  • duorc/SelfRC
  • squad

• Sentiment Analysis

  • amazon_polarity
  • imdb
  • rotten_tomatoes
  • yelp_review_full

• Natural Language Inference

  • anli
  • super_glue/cb

• Multiple-Choice Question Answering

  • app_reviews
  • cosmos_qa
  • dream
  • qasc
  • quail
  • quartz
  • race/all
  • social_i_qa
  • super_glue/boolq
  • super_glue/record
  • wiki_hop/original

• Text Generation

  • app_reviews
  • cnn_dailymail/3.0.0
  • dream
  • duorc/ParaphraseRC
  • duorc/SelfRC
  • gigaword
  • samsum

• Summarization

  • cnn_dailymail/3.0.0
  • duorc/ParaphraseRC
  • duorc/SelfRC
  • gigaword
  • multi_newspaws/labeled_final
  • samsum
  • xsum

• Paraphrase Generation & Identification

  • glue/mrpc
  • multi_newspaws/labeled_final

• Yes-No Question Answering

  • race/all
  • social_i_qa
  • super_glue/boolq

• Sentence Completion

  • hellaswag
  • super_glue/copa

• Textual Entailment

  • super_glue/rte

• Word Sense Disambiguation

  • super_glue/wic

• Coreference Resolution

  • super_glue/wsc.fixed

Citation

BibTeX:

@article{bonito:arxiv24,
  Author = {Nihal V. Nayak and Yiyang Nan and Avi Trost and Stephen H. Bach},
  Title = {Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation},
  Volume = {arXiv:2402.18334 [cs.CL]},
  Year = {2024}}