Dataset Card for Dataset Name

Dataset Summary

The CoT Collection is a dataset designed to induce Chain-of-Thought (CoT) capabilities into language models. While proprietary LLMs excel at generating Chain-of-Thoughts based on prompting, smaller LMs do not have this capability. Thus, by fine-tuning to generate Chain-of-Thoughts, it could acquire such abilities.

The CoT Collection provides 1.84 million Chain-of-Thoughts augmented across 1060 tasks from the Flan Collection.\ Experimental results show that fine-tuning on the CoT Collection results in (1) better zero-shot performance and (2) a better base model for few-shot learning.

We also provide a multilingual version of CoT Collection at this link.

Supported Tasks and Leaderboards

1060 tasks chosen from the Flan Collection.

The list of categories within the CoT Collection are:

Natural Language Inference
Extractive Question Answering
Closed Book Question Answering
Science
Toxic Classification
Arithmetic
Program Execution
Dialogue
Ethics
Commonsense Reasoning
Multiple Choice Question Answering

Languages

English

Dataset Structure

source: The input that is given to the language model (LM).
target: The ground truth answer to the source.
rationale: The Chain of Thought (CoT) that explains how the target could be derived from the source.
task: A category that shows which dataset the source and target was extracted from.

In our paper, we trained the underlying language model to generate in the following format:

\{rationale\}
[RESULT]
\{target\}

Then during evaluation, we parsed the prediction after the phrase [RESULT].

Data Splits

name	train
CoT-Collection	1837928

Citation Information

If you find the following model helpful, please considering citing our paper!

@article{kim2023cot,
  title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning},
  author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon},
  journal={arXiv preprint arXiv:2305.14045},
  year={2023}
}