GPIoT：为物联网程序合成与开发定制小语言模型
GPIoT: Tailoring Small Language Models for IoT Program Synthesis and Development
AI总结：
针对物联网代码生成中云端大模型存在的隐私、成本及领域知识不足问题，提出GPIoT系统，通过在物联网专用数据集上微调本地小语言模型，并构建IoTBench基准，有效提升了代码生成准确率与用户满意度。
摘要：
代码大型语言模型通过根据用户需求自动生成代码和文档，提升了软件开发效率。然而，当面对需要领域知识的物联网应用任务时，代码大型语言模型无法合成专业程序。虽然检索增强生成通过获取相关领域知识提供了一种有前景的解决方案，但它需要强大的云端大型语言模型（例如GPT-4）来处理用户需求和检索到的内容，这引发了重大的隐私担忧。这种方法还受到不稳定网络和高昂的大型语言模型查询成本的困扰。此外，确保检索内容的正确性和相关性也具有挑战性。为了解决这些问题，我们提出了GPIoT，这是一个用于物联网应用的代码生成系统，通过在物联网专用数据集上微调本地可部署的小语言模型来构建。小语言模型具有较小的模型规模，允许高效的本地部署和执行，从而缓解隐私担忧和网络不确定性。此外，通过使用我们的物联网专用数据集对小语言模型进行微调，小语言模型合成物联网相关程序的能力可以得到显著提升。为了评估GPIoT在合成物联网应用程序方面的能力，我们开发了一个基准测试IoTBench。大量实验和用户试用证明了GPIoT在生成物联网专用代码方面的有效性，其平均任务准确率提升了64.7%，并且用户满意度有显著改善。
英文摘要：
Code Large Language Models (LLMs) enhance software development efficiency by automatically generating code and documentation in response to user requirements. However, code LLMs cannot synthesize specialized programs when tasked with IoT applications that require domain knowledge. While Retrieval-Augmented Generation (RAG) offers a promising solution by fetching relevant domain knowledge, it necessitates powerful cloud LLMs (e.g., GPT-4) to process user requirements and retrieved contents, which raises significant privacy concerns. This approach also suffers from unstable networks and prohibitive LLM query costs. Moreover, it is challenging to ensure the correctness and relevance of the fetched contents. To address these issues, we propose GPIoT, a code generation system for IoT applications by fine-tuning locally deployable Small Language Models (SLMs) on IoT-specialized datasets. SLMs have smaller model sizes, allowing efficient local deployment and execution to mitigate privacy concerns and network uncertainty. Furthermore, by fine-tuning the SLMs with our IoT-specialized datasets, the SLMs' ability to synthesize IoT-related programs can be substantially improved. To evaluate GPIoT's capability in synthesizing programs for IoT applications, we develop a benchmark, IoTBench. Extensive experiments and user trials demonstrate the effectiveness of GPIoT in generating IoT-specialized code, outperforming state-of-the-art code LLMs with an average task accuracy increment of 64.7% and significant improvements in user satisfaction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
