
# 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

![plot](./cot_collection.JPG)

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.


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}
}
```
task_categories:text-generation task_categories:text-classification size_categories:1M<n<10M language:en license:cc-by-4.0 arxiv:2305.14045 region:us
{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-generation", "text-classification"]}
TAGS
#task_categories-text-generation #task_categories-text-classification #size_categories-1M<n<10M #language-English #license-cc-by-4.0 #arxiv-2305.14045 #region-us 

Dataset Card for Dataset Name
=============================


Dataset Description
-------------------


* Homepage:URL
* Repository:URL
* Paper:URL
* Point of Contact:seungone@URL


### Dataset Summary


!plot


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:


Then during evaluation, we parsed the prediction after the phrase .


### Data Splits



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



### Dataset Summary


!plot


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:


Then during evaluation, we parsed the prediction after the phrase . ### Data Splits



If you find the following model helpful, please considering citing our paper!
TAGS
#task_categories-text-generation #task_categories-text-classification #size_categories-1M<n<10M #language-English #license-cc-by-4.0 #arxiv-2305.14045 #region-us 
 ### Dataset Summary


!plot


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:


Then during evaluation, we parsed the prediction after the phrase . ### Data Splits



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