MBXP

MathQA-X

Dataset Summary

This repository contains data and code to perform execution-based multi-lingual evaluation of code generation capabilities and the corresponding data, namely, a multi-lingual benchmark MBXP, multi-lingual MathQA and multi-lingual HumanEval.
Results and findings can be found in the paper "Multi-lingual Evaluation of Code Generation Models".

Related Tasks and Leaderboards

Languages

The programming problems are written in multiple programming languages and contain English natural text in comments and docstrings.

Dataset Structure

To lookup currently supported datasets

get_dataset_config_names("mxeval/mathqa-x")
['python', 'java', 'javascript']

To load a specific dataset and language

from datasets import load_dataset
load_dataset("mxeval/mathqa-x", "python")
DatasetDict({
    test: Dataset({
        features: ['task_id', 'language', 'prompt', 'test', 'entry_point', 'canonical_solution'],
        num_rows: 1883
    })
})

Data Instances

An example of a dataset instance:

{
  "task_id": "MathQA/0",
  "language": "python",
  "prompt": "def problem():\n    \"\"\"\n    a shopkeeper sold an article offering a discount of 5 % and earned a profit of 31.1 % . what would have been the percentage of profit earned if no discount had been offered ? n0 = 5.0 n1 = 31.1\n    \"\"\"\n",
  "test": "import math\ndef compare(x, y):\n    return math.fabs(x-y)<1e-8\ncandidate = problem\nassert compare(candidate(), 38.0)\ndef check(x): pass\n",
  "entry_point": "problem",
  "canonical_solution": "    n0 = 5.0\n    n1 = 31.1\n    t0 = n1 + 100.0\n    t1 = 100.0 - n0\n    t2 = t0 * 100.0\n    t3 = t2 / t1\n    answer = t3 - 100.0\n    return answer\n"
}

Data Fields

task_id: identifier for the data sample
prompt: input for the model containing function header and docstrings
canonical_solution: solution for the problem in the prompt
description: task description
test: contains function to test generated code for correctness
entry_point: entry point for test
language: programming lanuage identifier to call the appropriate subprocess call for program execution

Data Splits

MathQA-X
- Python
- Java
- Javascript

Dataset Creation

Curation Rationale

Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps.

Personal and Sensitive Information

None.

Social Impact of Dataset

With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.

Execution

Execution Example

Install the repo mbxp-exec-eval to execute generations or canonical solutions for the prompts from this dataset.

>>> from datasets import load_dataset
>>> from mxeval.execution import check_correctness
>>> mathqa_python = load_dataset("mxeval/mathqa-x", "python", split="test")
>>> example_problem = mathqa_python[0]
>>> check_correctness(example_problem, example_problem["canonical_solution"], timeout=20.0)
{'task_id': 'MathQA/0', 'passed': True, 'result': 'passed', 'completion_id': None, 'time_elapsed': 9.673357009887695}

Considerations for Using the Data

Make sure to sandbox the execution environment.

Dataset Curators

AWS AI Labs

Licensing Information

LICENSE
THIRD PARTY LICENSES

Citation Information

@inproceedings{
athiwaratkun2023multilingual,
title={Multi-lingual Evaluation of Code Generation Models},
author={Ben Athiwaratkun and Sanjay Krishna Gouda and Zijian Wang and Xiaopeng Li and Yuchen Tian and Ming Tan and Wasi Uddin Ahmad and Shiqi Wang and Qing Sun and Mingyue Shang and Sujan Kumar Gonugondla and Hantian Ding and Varun Kumar and Nathan Fulton and Arash Farahani and Siddhartha Jain and Robert Giaquinto and Haifeng Qian and Murali Krishna Ramanathan and Ramesh Nallapati and Baishakhi Ray and Parminder Bhatia and Sudipta Sengupta and Dan Roth and Bing Xiang},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=Bo7eeXm6An8}
}

Contributions

skgouda@ benathi@