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Multi-Programming Language Sandbox for LLMs

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arxiv 2410.23074 v2 pith:7O7CNFZZ submitted 2024-10-30 cs.SE cs.CL

Multi-Programming Language Sandbox for LLMs

Shihan Dou , Jiazheng Zhang , Jianxiang Zang , Yunbo Tao , Weikang Zhou , Haoxiang Jia , Shichun Liu , Yuming Yang
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Zhiheng Xi Shenxi Wu Shaoqing Zhang Muling Wu Changze Lv Limao Xiong Wenyu Zhan Lin Zhang Rongxiang Weng Jingang Wang Xunliang Cai Yueming Wu Ming Wen Rui Zheng Tao Ji Yixin Cao Tao Gui Xipeng Qiu Qi Zhang Xuanjing Huang
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classification cs.SE cs.CL
keywords mplsandboxcodelanguageanalysiscode-relatedllm-basedllmstasks
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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 also integrates both traditional and LLM-based code analysis tools, providing a comprehensive analysis of generated code. MPLSandbox can be effortlessly integrated into the training and deployment of LLMs to improve the quality and correctness of their 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 integrate it into training and deployment approaches, and also employ it to optimize workflows for a wide range of real-world code-related tasks. Our goal is to enhance researcher productivity on LLM-based code-related tasks by simplifying and automating workflows through delegation to MPLSandbox.

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