# mxeval **Repository Path**: woohello/mxeval ## Basic Information - **Project Name**: mxeval - **Description**: Multi-Lingual HumanEval - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-09-01 - **Last Updated**: 2026-09-01 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Execution-based evaluation of code in 10+ languages This repository contains 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" (https://arxiv.org/abs/2210.14868). ## Paper summary Our paper describes the language conversion framework, the synthetic solution generation, and many other types of evaluation beyond the traditional function completion evaluation such as translation, code insertion, summarization, and robustness evaluation. ![Paper summary](./graphics/paper_summary.png) ## Language conversion of execution-based function completion datasets Below we demonstrate the language conversion (component A above) for the conversion from Python to Java (abridged example for brevity). ![Example conversion to Java](./graphics/mbxp_java_conversion.png) # Installation Check out and install this repository: ``` git clone https://github.com/amazon-science/mxeval.git pip install -e mxeval ``` ## Dependencies We provide scripts to help set up programming language dependencies that are used to execute and evaluate using datasets in MBXP. ### Amazon Linux AMI ``` bash language_setup/amazon_linux_ami.sh ``` ### Ubuntu ``` bash language_setup/ubuntu.sh ``` # Usage **This program exists to run untrusted model-generated code. Users are strongly encouraged not to do so outside of a robust security sandbox. See the comment in `execution.py` for more information and instructions.** Each sample is formatted into a single line: ``` {"task_id": "Corresponding task ID", "completion": "Completion only without the prompt", "language": "programming language name"} ``` We provide `data/mbxp/examples/mbxp_samples.jsonl` to illustrate the format. Here is nearly functional example code (you just have to provide `generate_one_completion` to make it work) that saves generated completions to `samples.jsonl`. ``` from mxeval.data import write_jsonl, read_problems problems = read_problems() num_samples_per_task = 200 samples = [ dict(task_id=task_id, language=problems[task_id]["language"], completion=generate_one_completion(problems[task_id]["prompt"])) for task_id in problems for _ in range(num_samples_per_task) ] write_jsonl("samples.jsonl", samples) ``` To evaluate the samples for, e.g., Java MBJP evaluation, run ``` evaluate_functional_correctness data/mbxp/examples/mbjp_samples.jsonl --problem_file data/mbxp/mbjp_release_v1.jsonl ``` or to run all languages ``` for lang in mbcpp mbcsp mbgp mbjp mbjsp mbkp mbphp mbplp mbpp mbrbp mbscp mbswp mbtsp; do evaluate_functional_correctness --problem_file data/mbxp/${lang}_release_v1.jsonl data/mbxp/examples/${lang}_samples.jsonl; done ``` You can check the programming-language dependency installation by running the above example for each MBXP dataset. You should obtain the following results for the mbxp_samples.jsonl files provided: | Dataset | pass@1 | |---------|--------| | MBCPP | 79.60% | | MBCSP | 63.63% | | MBGP | 39.19% | | MBJP | 85.30% | | MBJSP | 78.67% | | MBKP | 63.77% | | MBPHP | 72.77% | | MBPLP | 38.41% | | MBPP | 82.24% | | MBRBP | 58.90% | | MBSCP | 42.96% | | MBSWP | 29.40% | | MBTSP | 87.29% | Note: Because there is no unbiased way of estimating pass@k when there are fewer samples than k, the script does not evaluate pass@k for these cases. To evaluate with other k values, pass `--k `. For other options, see ``` $ evaluate_functional_correctness --help ``` However, we recommend that you use the default values for the rest. ### Example usage with non-default values ``` evaluate_functional_correctness data/mbxp/samples/mbjp_samples.jsonl --problem_file data/mbxp/mbjp_release_v1.jsonl --n_workers 63 --k 1,5,10,100 ``` ### Known Issues While evaluation uses very little memory, you might see the following error message when the system is running out of RAM. Since this may cause some correct programs to fail, we recommend that you free some memory and try again. ``` malloc: can't allocate region ``` Some system might require longer compilation timeout. If you see that the execution fails due to compilation timeout reason, this number should be increased accordingly. ### Canonical solutions release We have released canonical solutions for certain popular langauges (v1.2). The detailed numbers of the solutions for each langauge are listed below. | Dataset | # solutions | # problems | |---------|-------------|------------| | MBCPP | 773 | 848 | | MBCSP | 725 | 968 | | MBJP | 874 | 966 | | MBJSP | 938 | 966 | | MBKP | 796 | 966 | | MBPHP | 950 | 966 | | MBPP | 960 | 974 | | MBRBP | 784 | 966 | | MBTSP | 967 | 968 | ### Future release We plan to release synthetic canonical solutions as well as processed datasets for other evaluation tasks such as code-insertion, code-translation, etc. ### Credits We adapted OpenAI's human-eval package (https://github.com/openai/human-eval) for the multi-lingual case. We thank OpenAI for their pioneering effort in this field including the release of the original HumanEval dataset, which we convert to the multi-lingual versions. We also thank Google for their release of the original MBPP Python dataset (https://github.com/google-research/google-research/tree/master/mbpp), which we adapt and convert to other programming languages. ### Citation Please cite using the following bibtex entry: ``` @article{mbxp_athiwaratkun2022, title = {Multi-lingual Evaluation of Code Generation Models}, author = {Athiwaratkun, Ben and Gouda, Sanjay Krishna and Wang, Zijian and Li, Xiaopeng and Tian, Yuchen and Tan, Ming and Ahmad, Wasi Uddin and Wang, Shiqi and Sun, Qing and Shang, Mingyue and Gonugondla, Sujan Kumar and Ding, Hantian and Kumar, Varun and Fulton, Nathan and Farahani, Arash and Jain, Siddhartha and Giaquinto, Robert and Qian, Haifeng and Ramanathan, Murali Krishna and Nallapati, Ramesh and Ray, Baishakhi and Bhatia, Parminder and Sengupta, Sudipta and Roth, Dan and Xiang, Bing}, doi = {10.48550/ARXIV.2210.14868}, url = {https://arxiv.org/abs/2210.14868}, keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```