Leveraging automated knowledge transfer to enable smart home planning capabilities of small language model

Smart home device control is a difficult task if the instruction is abstract and the planner needs to adjust dynamic home configurations. With the increasing capability of Large Language Model (LLM), they have become the customary model for zero-shot planning tasks similar to smart home device control. Although cloud supported large language models can seamlessly do device control tasks, on-device small language models show limited capabilities. In this work, we show how we can leverage large language models to enable small language models for device control task. Towards this goal, we develop an automated system to generate device control planning data leveraging large language model and use the generated data to finetune the small language models. We empirically validate the improvement of small language models’ performance for device control task.