The proliferation of smart home devices has increased the complexity of controlling and managing them, leading to user fatigue. In this context, large language models (LLMs) offer a promising solution by enabling natural-language interfaces for Internet of Things (IoT) control. However, existing LLM-based approaches suffer from unreliable and inefficient device control due to the non-deterministic nature of LLMs, high inference latency and cost, and limited personalization. To address these challenges, we present IoTGPT, an LLM-based smart home agent designed to execute IoT commands in a reliable, efficient, and personalized manner. Inspired by how humans manage complex tasks, IoTGPT decomposes user instructions into subtasks and memorizes them. By reusing learned subtasks, subsequent instructions can be processed more efficiently with fewer LLM calls, improving reliability and reducing both latency and cost. IoTGPT also supports fine-grained personalization by adapting individual subtasks to user preferences. Our evaluation demonstrates that IoTGPT outperforms baselines in accuracy, latency/cost, and personalization, while reducing user workload.

翻译：智能家居设备的激增增加了控制与管理复杂性，导致用户产生使用疲劳。在此背景下，大语言模型（LLMs）通过为物联网（IoT）控制提供自然语言界面，展现出极具前景的解决方案。然而，现有基于LLM的方法因LLM的非确定性本质、高推理延迟与成本以及有限的个性化能力，导致设备控制不可靠且低效。为应对这些挑战，我们提出了IoTGPT——一种基于LLM的智能家居代理，旨在以可靠、高效且个性化的方式执行物联网指令。受人类处理复杂任务方式的启发，IoTGPT将用户指令分解为子任务并进行记忆存储。通过复用已学习的子任务，后续指令能够以更少的LLM调用次数实现高效处理，从而提升可靠性并降低延迟与成本。IoTGPT还支持通过适配用户偏好对单个子任务进行细粒度个性化调整。实验评估表明，IoTGPT在准确性、延迟/成本及个性化方面均优于基线方法，同时有效降低了用户操作负担。