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.

翻译：本文介绍MPLSandbox，一个开箱即用的多编程语言沙箱，旨在为大型语言模型（LLMs）提供来自编译器和分析工具的统一且全面的反馈。它能够自动识别代码的编程语言，并在隔离的子沙箱中编译和执行代码，以确保安全性和稳定性。此外，MPLSandbox还集成了传统和基于LLM的代码分析工具，对生成的代码进行全面分析。MPLSandbox可以轻松集成到LLMs的训练和部署中，以提高其生成代码的质量和正确性。它还有助于研究人员简化各种基于LLM的代码相关任务的工作流程，降低开发成本。为了验证MPLSandbox的有效性，我们将其集成到训练和部署方法中，并利用它来优化广泛的实际代码相关任务的工作流程。我们的目标是通过将工作流程委托给MPLSandbox进行简化和自动化，从而提高研究人员在基于LLM的代码相关任务上的生产力。