@inproceedings{dou-etal-2025-multi,
title = "Multi-Programming Language Sandbox for {LLM}s",
author = "Dou, Shihan and
Zhang, Jiazheng and
Zang, Jianxiang and
Tao, Yunbo and
Zhou, Weikang and
Jia, Haoxiang and
Liu, Shichun and
Yang, Yuming and
Wu, Shenxi and
Xi, Zhiheng and
Wu, Muling and
Zheng, Rui and
Lv, Changze and
Xiong, Limao and
Zhang, Shaoqing and
Zhang, Lin and
Zhan, Wenyu and
Weng, Rongxiang and
Wang, Jingang and
Cai, Xunliang and
Wu, Yueming and
Wen, Ming and
Cao, Yixin and
Gui, Tao and
Qiu, Xipeng and
Zhang, Qi and
Huang, Xuanjing",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.5/",
doi = "10.18653/v1/2025.acl-demo.5",
pages = "40--50",
ISBN = "979-8-89176-253-4",
abstract = "We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability. In addition, MPLSandbox integrates both traditional and LLM-based code analysis tools, providing a comprehensive analysis of generated code. It also can be effortlessly integrated into the training and deployment of LLMs to improve the quality and correctness of generated code. It also helps researchers streamline their workflows for various LLM-based code-related tasks, reducing the development cost. To validate the effectiveness of MPLSandbox, we conduct extensive experiments by integrating it into several training and deployment scenarios, and employing it to optimize workflows for a wide range of downstream code tasks. Our goal is to enhance researcher productivity on LLM-based code tasks by simplifying and automating workflows through delegation to MPLSandbox."
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<abstract>We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability. In addition, MPLSandbox integrates both traditional and LLM-based code analysis tools, providing a comprehensive analysis of generated code. It also can be effortlessly integrated into the training and deployment of LLMs to improve the quality and correctness of generated code. It also helps researchers streamline their workflows for various LLM-based code-related tasks, reducing the development cost. To validate the effectiveness of MPLSandbox, we conduct extensive experiments by integrating it into several training and deployment scenarios, and employing it to optimize workflows for a wide range of downstream code tasks. Our goal is to enhance researcher productivity on LLM-based code tasks by simplifying and automating workflows through delegation to MPLSandbox.</abstract>
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%0 Conference Proceedings
%T Multi-Programming Language Sandbox for LLMs
%A Dou, Shihan
%A Zhang, Jiazheng
%A Zang, Jianxiang
%A Tao, Yunbo
%A Zhou, Weikang
%A Jia, Haoxiang
%A Liu, Shichun
%A Yang, Yuming
%A Wu, Shenxi
%A Xi, Zhiheng
%A Wu, Muling
%A Zheng, Rui
%A Lv, Changze
%A Xiong, Limao
%A Zhang, Shaoqing
%A Zhang, Lin
%A Zhan, Wenyu
%A Weng, Rongxiang
%A Wang, Jingang
%A Cai, Xunliang
%A Wu, Yueming
%A Wen, Ming
%A Cao, Yixin
%A Gui, Tao
%A Qiu, Xipeng
%A Zhang, Qi
%A Huang, Xuanjing
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F dou-etal-2025-multi
%X We introduce MPLSandbox, an out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs). It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability. In addition, MPLSandbox integrates both traditional and LLM-based code analysis tools, providing a comprehensive analysis of generated code. It also can be effortlessly integrated into the training and deployment of LLMs to improve the quality and correctness of generated code. It also helps researchers streamline their workflows for various LLM-based code-related tasks, reducing the development cost. To validate the effectiveness of MPLSandbox, we conduct extensive experiments by integrating it into several training and deployment scenarios, and employing it to optimize workflows for a wide range of downstream code tasks. Our goal is to enhance researcher productivity on LLM-based code tasks by simplifying and automating workflows through delegation to MPLSandbox.
%R 10.18653/v1/2025.acl-demo.5
%U https://aclanthology.org/2025.acl-demo.5/
%U https://doi.org/10.18653/v1/2025.acl-demo.5
%P 40-50
Markdown (Informal)
[Multi-Programming Language Sandbox for LLMs](https://aclanthology.org/2025.acl-demo.5/) (Dou et al., ACL 2025)
ACL
- Shihan Dou, Jiazheng Zhang, Jianxiang Zang, Yunbo Tao, Weikang Zhou, Haoxiang Jia, Shichun Liu, Yuming Yang, Shenxi Wu, Zhiheng Xi, Muling Wu, Rui Zheng, Changze Lv, Limao Xiong, Shaoqing Zhang, Lin Zhang, Wenyu Zhan, Rongxiang Weng, Jingang Wang, Xunliang Cai, Yueming Wu, Ming Wen, Yixin Cao, Tao Gui, Xipeng Qiu, Qi Zhang, and Xuanjing Huang. 2025. Multi-Programming Language Sandbox for LLMs. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 40–50, Vienna, Austria. Association for Computational Linguistics.