---
title: Multilingual-CoT-Collection
canonical_url: "https://www.modelscope.cn/datasets/AI-ModelScope/Multilingual-CoT-Collection"
md_url: "https://www.modelscope.cn/datasets/AI-ModelScope/Multilingual-CoT-Collection.md"
repository: AI-ModelScope/Multilingual-CoT-Collection
last_updated: 2024-08-23
license: cc-by-4.0
storage_size: "703 MB"
domain:
  - configs
  - size_categories
  - language
  - task_categories
tasks:
  - "{config_name=zh, data_files=./data/CoT_collection_zh.json}"
  - "100K<n<1M"
  - en
  - text-classification
downloads: 3601
stars: 2
---

# Multilingual-CoT-Collection

> Multilingual-CoT-Collection - AI-ModelScope 在 ModelScope 开源的数据集。Dataset Card for Dataset Name

AI-ModelScope/Multilingual-CoT-Collection 是 ModelScope 魔搭社区上的{config_name=zh, data_files=./data/CoT_collection_zh.json}、100K<n<1M、en数据集，涉及 configs、size_categories、language 领域，存储大小 703 MB，采用 cc-by-4.0 许可。

- **Repository**: AI-ModelScope/Multilingual-CoT-Collection
- **License**: cc-by-4.0
- **Tasks**: {config_name=zh, data_files=./data/CoT_collection_zh.json}, 100K<n<1M, en, text-classification
- **Domain**: configs, size_categories, language, task_categories
- **Storage size**: 703 MB
- **Downloads**: 3601
- **Stars**: 2
- **Last updated**: 2024-08-23

Source: https://www.modelscope.cn/datasets/AI-ModelScope/Multilingual-CoT-Collection

---

# 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 Multilingual CoT Collection is a dataset designed to induce Chain-of-Thought (CoT) capabilities into multilingual 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 Multilingual 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}
}
```