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title: README
emoji: 🔥
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colorTo: purple
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# EvalPlus: Rigorous Evaluation of LLMs for Code Generation

## About

EvalPlus evaluates LLM-generated code on:

* Code Correctness: HumanEval+ and MBPP+
* Code Efficiency: EvalPerf

## Resources

* 💻 **GitHub Repo**: [evalplus/evalplus](https://github.com/evalplus/evalplus)
* 🏆 **Leader Board**: [evalplus.github.io](https://evalplus.github.io)
* 📜 **Papers**: [EvalPlus@NeurIPS'23](https://arxiv.org/abs/2305.01210), [EvalPerf@COLM'24](https://arxiv.org/abs/2408.06450)
* 🐍 **Python Package**: [PyPI](https://pypi.org/project/evalplus/)

## Citations

```bibtex
@inproceedings{evalplus,
  title = {Is Your Code Generated by Chat{GPT} Really Correct? Rigorous Evaluation of Large Language Models for Code Generation},
  author = {Liu, Jiawei and Xia, Chunqiu Steven and Wang, Yuyao and Zhang, Lingming},
  booktitle = {Thirty-seventh Conference on Neural Information Processing Systems},
  year = {2023},
  url = {https://openreview.net/forum?id=1qvx610Cu7},
}

@inproceedings{evalperf,
  title = {Evaluating Language Models for Efficient Code Generation},
  author = {Liu, Jiawei and Xie, Songrun and Wang, Junhao and Wei, Yuxiang and Ding, Yifeng and Zhang, Lingming},
  booktitle = {First Conference on Language Modeling},
  year = {2024},
  url = {https://openreview.net/forum?id=IBCBMeAhmC},
}
```