---
license: apache-2.0
language:
- sw
- bn
- te
- th
- ja
- zh
- ru
- es
- fr
- de
- en
tags:
- text-generation
---
# mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models

Paper: https://arxiv.org/abs/2406.02301

Code: https://github.com/laihuiyuan/mCoT

Dataset: https://huggingface.co/datasets/laihuiyuan/mCoT-MATH

### Introduction
We introduce mCoT, a 7B parameter model for multilingual math reasoning that achieves impressive multilingual reasoning consistency across multiple languages. 
Based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1), mCoT is trained on [mCoT-MATH](https://huggingface.co/datasets/laihuiyuan/mCoT-MATH), the first large-scale multilingual math CoT reasoning dataset containing around 6.3 million samples for 11 diverse languages.


### Results on [MGSM](https://arxiv.org/abs/2210.03057v1)
| Language               | SW   | BN   | TE   | TH   | JA   | ZH   | RU   | ES   | FR   | DE   | EN   | 
|:-----------------------|:-----|:-----|:-----|:-----|:-----|:-----|:-----|:-----|:-----|:-----|:-----|
| GPT-3 few-shot         | 11.2 | 6.4  | 0.4  | 0.8  | 26.0 | 40.0 | 28.4 | 40.4 | 37.6 | 36.0 | 53.6 |
| GPT-3.5-En 2-shot      | 40.0 | 7.6  | -    | 15.6 | 46.8 | 52.8 | 50.4 | 61.2 | 59.2 | 62.0 | 67.2 |
| GPT4-En 2-shot         | 64.4 | 17.6 | -    | 40.4 | 71.6 | 70.0 | 64.0 | 71.2 | 72.0 | 73.6 | 80.0 |
| PaLM-540B few-shot     | 35.2 | 46.0 | 45.6 | 52.8 | 40.0 | 46.8 | 48.4 | 56.8 | 46.4 | 49.2 | 62.4 |
| WizardMath-7B          | 3.4  | 2.0  | -    | 4.0  | 24.0 | 22.4 | 30.8 | 34.8 | 30.4 | 30.4 | 47.6 |
| MathOctopus-7B         | 38.4 | 33.2 | -    | 36.4 | 35.6 | 45.2 | 48.4 | 45.2 | 38.0 | 43.6 | 54.8 |
| MathOctopus-Mistral-7B | 51.6 | 44.0 | -    | 48.8 | 48.0 | 51.6 | 49.6 | 53.2 | 47.2 | 50.0 | 58.4 |
| xCoT-7B                | 48.4 | 40.4 | 42.8 | 49.2 | 50.0 | 50.0 | 50.0 | 48.8 | 49.6 | 47.2 | 48.4 |
| WizardMath-13B         | 5.6  | 6.4  | -    | 5.6  | 22.0 | 28.0 | 34.4 | 45.6 | 42.0 | 40.4 | 52.8 |
| MathOctopus-13B        | 46.0 | 42.0 | -    | 46.0 | 39.6 | 51.2 | 47.6 | 53.2 | 49.6 | 49.2 | 51.6 |
| xCoT-13B               | 51.6 | 50.0 | 47.2 | 50.0 | 49.6 | 54.0 | 56.8 | 54.8 | 46.4 | 52.4 | 54.4 |
| mCoT-7B                | 67.2 | 65.6 | 62.4 | 67.6 | 65.2 | 64.8 | 66.8 | 68.4 | 63.8 | 61.2 | 71.6 |


### Results on [MSVAMP](https://arxiv.org/abs/2310.20246)
| Language               | SW   | BN   | TH   | JA   | ZH   | RU   | ES   | FR   | DE   | EN   | AVG  | 
|:-----------------------|:-----|:-----|:-----|:-----|:-----|:-----|:-----|:-----|:-----|:-----|:-----|
| GPT-3.5-En zero-shot   | 63.2 | 3.1  | 24.4 | 63.3 | 72.4 | 62.3 | 69.5 | 71.9 | 66.7 | 76.1 | 57.3 |
| GPT-3.5-En 2-shot      | 68.4 | 14.4 | 46.0 | 74.0 | 78.4 | 70.9 | 74.6 | 78.2 | 73.9 | 81.2 | 66.0 |
| GPT4-En 2-shot         | 75.7 | 31.2 | 68.1 | 74.8 | 78.9 | 77.9 | 81.5 | 83.9 | 78.1 | 80.1 | 73.0 |
| PaLM-540B few-shot     | 35.2 | 46.0 | 45.6 | 52.8 | 40.0 | 46.8 | 48.4 | 56.8 | 46.4 | 49.2 | 62.4 |
| WizardMath-7B          | 10.3 | 16.1 | 6.3  | 26.7 | 26.8 | 33.7 | 42.9 | 39.9 | 39.6 | 45.1 | 27.0 |
| MathOctopus-7B         | 42.3 | 32.8 | 40.5 | 43.2 | 43.2 | 42.1 | 44.5 | 45.3 | 43.1 | 46.8 | 42.4 |
| MathOctopus-Mistral-7B | 41.2 | 36.7 | 40.2 | 41.5 | 43.1 | 44.0 | 47.0 | 49.0 | 46.4 | 49.7 | 43.9 |
| WizardMath-13B         | 12.5 | 13.7 | 16.3 | 29.5 | 37.0 | 43.8 | 50.4 | 49.4 | 48.7 | 56.3 | 35.8 |
| MathOctopus-13B        | 43.4 | 34.2 | 39.5 | 43.1 | 46.4 | 48.2 | 48.2 | 49.9 | 47.7 | 44.6 | 44.5 |
| mCoT-7B                | 55.0 | 53.7 | 56.4 | 58.8 | 58.2 | 58.1 | 58.9 | 58.8 | 61.1 | 58.3 | 57.7 |

### Prompt Template
```bash
# Template
template = "Question: \n{question} \nAnswer: \n{language}\n"

# Language prompt
bn = "আসুন ধাপে ধাপে চিন্তা করি।"
de = "Denken wir Schritt für Schritt."
en = "Let's think step by step."
es = "Pensemos paso a paso."
fr = "Réfléchissons étape par étape."
ja = "段階的に考えてみましょう。"
ru = "Давайте думать поэтапно."
sw = "Hebu fikiria hatua kwa hatua."
te = "అంచెలంచెలుగా ఆలోచిద్దాం."
th = "ลองคิดทีละขั้นตอน"
zh = "让我们一步步思考。"

# Math question
math_en = "A robe takes 2 bolts of blue fiber and half that much white fiber.  How many bolts in total does it take?"

# An example for the English question
prompt = template.format(question=math_en, language=en)
```

### Citation
If you use any content from this repository, please cite our paper:
```
@inproceedings{lai-etal-2024-mcot,
    title = "mCoT: Multilingual Instruction Tuning for Reasoning Consistency
    in Language Models",
    author = "Lai, Huiyuan and Nissim, Malvina",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association
    for Computational Linguistics,
    month = aug,
    address = "Bangkok, Thailand",
    year = "2024",
    publisher = "Association for Computational Linguistics"
}
```