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| title: README |
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| # EvalPlus: Rigorous Evaluation of LLMs for Code Generation |
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| ## About |
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| EvalPlus evaluates LLM-generated code on: |
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| * Code Correctness: HumanEval+ and MBPP+ |
| * Code Efficiency: EvalPerf |
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| ## Resources |
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| * 💻 **GitHub Repo**: [evalplus/evalplus](https://github.com/evalplus/evalplus) |
| * 🏆 **Leader Board**: [evalplus.github.io](https://evalplus.github.io/leaderboard.html) |
| * 📜 **NeurIPS Paper**: [OpenReview](https://openreview.net/pdf?id=1qvx610Cu7) |
| * 🐍 **Python Package**: [PyPI](https://pypi.org/project/evalplus/) |
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| ## Citations |
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| ```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}, |
| } |
| ``` |
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