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---
datasets:
- TIGER-Lab/MathInstruct
language:
- en
license: apache-2.0
metrics:
- accuracy
pipeline_tag: text-generation
---
# How Do Humans Write Code? Large Models Do It the Same Way Too
Paper: [https://arxiv.org/pdf/2402.15729](https://arxiv.org/pdf/2402.15729)
Code: [https://github.com/seamoke/Human-Think-Language](https://github.com/seamoke/Human-Think-Language)
## Introduction
For this model, please sure your transformers>=4.39.2.
We introduce HTL, a model which utilizes the complete reasoning process of CoT to enhance PoT. This model was secondarily fine-tuned based on [MAmmoTH-Coder-7B](https://huggingface.co/TIGER-Lab/MAmmoTH-Coder-7B)
## Evaluation
The models are evaluated using open-ended and multiple-choice math problems from several datasets. Here are the results:
| **Model** | **GSM** |**GSM-Hard** | **NumGLUE** | **MATH** | **Sim** | **SVAMP** | **MAWPS** | **ASDiV** |
|---------------------------| ----------|---------------|---------------|-----------|----------|---------- |------------|---------------|
| **MAmmoTH-Coder-7B** | 59.4 |56.3 | 66.4 |33.4| 45.9 | 70.7 | 91.9 | 69.3 |
| **TORA** | **72.6** |56.0 | 46.2 |**44.6**| 48.5 | 70.4 | 91.3 | **78.7** |
| **MAmmoTH-Coder-7B** | 65.7 |**58.3** | **75.1** |34.9| **50.8** | **74.4** | **94.2** | 73.1 |
## Prompt Format
If you want to do HTL:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
I'd like you to solve this problem in 3 steps:
1.Answer the question in plain language without writing any code.\n
2.Output one line of *\n.
3.Write program code based on the solution process in step 1 to solve the problem.\n
### Instruction:
{query}
Let's write a program.
### Response:"
```
## Citation
If you use the models, data, or code from this project, please cite the original paper:
```
@article{li2024humans,
title={How Do Humans Write Code? Large Models Do It the Same Way Too},
author={Li, Long},
journal={arXiv preprint arXiv:2402.15729},
year={2024}
}
``` |