README.md

---
pretty_name: MBPP-no-resource
license: mit
language:
- en
task_categories:
- text-generation
tags:
- benchmark
- evaluation
- multilingual
- code-generation
- low-resource
- no-resource
dataset_info:
  features:
  - name: task_id
    dtype: int64
  - name: original_mbpp_id
    dtype: string
  - name: prompt
    dtype: string
  - name: tests
    dtype: string
  splits:
  - name: gleam
    num_bytes: 479681
    num_examples: 355
  - name: moonbit
    num_bytes: 139083
    num_examples: 355
  download_size: 161230
  dataset_size: 618764
configs:
- config_name: default
  data_files:
  - split: gleam
    path: data/gleam-*
  - split: moonbit
    path: data/moonbit-*
---


# MBPP Benchmark for Gleam and MoonBit

## Dataset Description
This dataset contains a translation of the **MBPP** benchmark into two no-resource programming languages: **Gleam** and **MoonBit**.

This dataset is derived from the [**MultiPL-E**](https://huggingface.co/datasets/nuprl/MultiPL-E) benchmark, which extends MBPP by translating its programming tasks into multiple programming languages. 

More details on this benchmark and how it was created can be found in the paper [No Resource, No Benchmarks, No Problem? Evaluating and Improving LLMs for Code Generation in No-Resource Languages](https://huggingface.co/papers/2606.16827).

## Dataset Statistics

- Number of tasks: **355**
- Programming languages: **2**
- Total examples: **710**
- Source benchmark: **MBPP**
- Intermediate benchmark: **MultiPL-E**

## Supported Languages

- Gleam
- MoonBit

## Dataset Structure

Each example contains the following fields:

| Field | Description |
|-------|-------------|
| `task_id` | Identifier of the translated task in this benchmark. |
| `original_mbpp_id` | Identifier of the task in the original MBPP benchmark. |
| `prompt` | Signature and docstring of the function to be implemented. |
| `tests` | Test suite used to evaluate the correctness of generated solutions. |

## Usage
To load this benchmark, you can use the `datasets` library as follows.

First, install the library if you haven't already:

```bash
pip install datasets
```

Then, load the dataset:

```python
from datasets import load_dataset

dataset = load_dataset("Devy1/mbpp-no-resource")

# Load the Gleam split
gleam_split = dataset["gleam"]
print(gleam_split[0])
```

Or load only a specific language:

```python
gleam_ds = load_dataset("Devy1/mbpp-no-resource", split="gleam")
```

> **Note:** Executing the test suites requires the corresponding language environments and dependencies. The complete generation and evaluation pipeline, including setup instructions for Gleam and MoonBit, is available in our [GitHub repository](https://github.com/Devy99/no-resource-pl-study).

## Citation

If you use this dataset, please cite our work:

```bibtex
@article{giagnorio2026noresource,
  title={No Resource, No Benchmarks, No Problem? Evaluating and Improving LLMs for Code Generation in No-Resource Languages},
  author={Giagnorio, Alessandro and Martin-Lopez, Alberto and Bavota, Gabriele},
  journal={IEEE Transactions on Software Engineering},
  year={2026},
  publisher={IEEE}
}
```

Since this dataset is derived from MBPP through MultiPL-E, please also cite the original benchmark papers:

```bibtex
@article{austin2021program,
  title={Program synthesis with large language models},
  author={Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and others},
  journal={arXiv preprint arXiv:2108.07732},
  year={2021}
}
```

```bibtex
@article{cassano2023multipl,
  title={Multipl-e: A scalable and polyglot approach to benchmarking neural code generation},
  author={Cassano, Federico and Gouwar, John and Nguyen, Daniel and Nguyen, Sydney and Phipps-Costin, Luna and Pinckney, Donald and Yee, Ming-Ho and Zi, Yangtian and Anderson, Carolyn Jane and Feldman, Molly Q and others},
  journal={IEEE Transactions on Software Engineering},
  volume={49},
  number={7},
  pages={3675--3691},
  year={2023},
  publisher={IEEE}
}
```

### Paper

* Hugging Face Papers: [https://huggingface.co/papers/2606.16827](https://huggingface.co/papers/2606.16827)