The advent of Large Language Models (LLMs) has profoundly transformed our lives, revolutionizing interactions with AI and lowering the barrier to AI usage. While LLMs are primarily designed for natural language interaction, the extensive embedded knowledge empowers them to comprehend digital sensor data. This capability enables LLMs to engage with the physical world through IoT sensors and actuators, performing a myriad of AIoT tasks. Consequently, this evolution triggers a paradigm shift in conventional AIoT application development, democratizing its accessibility to all by facilitating the design and development of AIoT applications via natural language. However, some limitations need to be addressed to unlock the full potential of LLMs in AIoT application development. First, existing solutions often require transferring raw sensor data to LLM servers, which raises privacy concerns, incurs high query fees, and is limited by token size. Moreover, the reasoning processes of LLMs are opaque to users, making it difficult to verify the robustness and correctness of inference results. This paper introduces AutoIOT, an LLM-based automated program generator for AIoT applications. AutoIOT enables users to specify their requirements using natural language (input) and automatically synthesizes interpretable programs with documentation (output). AutoIOT automates the iterative optimization to enhance the quality of generated code with minimum user involvement. AutoIOT not only makes the execution of AIoT tasks more explainable but also mitigates privacy concerns and reduces token costs with local execution of synthesized programs. Extensive experiments and user studies demonstrate AutoIOT's remarkable capability in program synthesis for various AIoT tasks. The synthesized programs can match and even outperform some representative baselines.

翻译：大型语言模型（LLM）的出现深刻改变了我们的生活，革新了人机交互方式并降低了人工智能的使用门槛。尽管LLM主要设计用于自然语言交互，但其内嵌的广泛知识使其能够理解数字传感器数据。这一能力使LLM能够通过物联网传感器和执行器与物理世界交互，执行各类AIoT任务。因此，这一演进引发了传统AIoT应用开发范式的转变，通过自然语言促进AIoT应用的设计与开发，使其普及化成为可能。然而，要充分释放LLM在AIoT应用开发中的潜力，仍需解决若干局限性。首先，现有方案通常需要将原始传感器数据传输至LLM服务器，这引发了隐私担忧、产生高昂查询费用，并受限于令牌数量。此外，LLM的推理过程对用户不透明，难以验证推理结果的鲁棒性与正确性。本文提出AutoIOT，一种基于LLM的AIoT应用自动化程序生成器。AutoIOT允许用户使用自然语言（输入）指定需求，并自动生成带有文档说明的可解释程序（输出）。该系统通过自动化迭代优化，以最小用户参与提升生成代码质量。AutoIOT不仅使AIoT任务的执行更具可解释性，还能通过本地执行合成程序来缓解隐私担忧并降低令牌成本。大量实验与用户研究表明，AutoIOT在各类AIoT任务的程序合成中具有卓越能力，其生成程序的表现可媲美甚至超越部分代表性基线方法。