| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-generation |
| - text-classification |
| language: |
| - en |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # Dataset Card for Dataset Name |
|
|
| ## Dataset Description |
|
|
| - **Homepage:https://github.com/kaistAI/CoT-Collection** |
| - **Repository:https://github.com/kaistAI/CoT-Collection** |
| - **Paper:https://arxiv.org/abs/2305.14045** |
| - **Point of Contact:seungone@kaist.ac.kr** |
|
|
| ### 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](https://huggingface.co/datasets/kaist-ai/Multilingual-CoT-Collection). |
|
|
| ### Supported Tasks and Leaderboards |
|
|
| 1060 tasks chosen from the Flan Collection. |
|
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|
|
| 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| |
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|
|
| ### 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} |
| } |
| ``` |
