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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.

While Transformer-based pre-trained language models and their variants exhibit strong semantic representation capabilities, the question of comprehending the information gain derived from the additional components of PLMs remains an open question in this field. Motivated by recent efforts that prove Multilayer-Perceptrons (MLPs) modules achieving robust structural capture capabilities, even outperforming Graph Neural Networks (GNNs), this paper aims to quantify whether simple MLPs can further enhance the already potent ability of PLMs to capture linguistic information.

The meaning of complex phrases in natural language is composed of their individual components. The task of compositional generalization evaluates a model's ability to understand new combinations of components. Previous studies trained smaller, task-specific models, which exhibited poor generalization. While large language models (LLMs) exhibit impressive generalization abilities on many tasks through in-context learning (ICL), their potential for compositional generalization remains unexplored.

Neural semantic parsing maps natural languages (NL) to equivalent formal semantics which are compositional and deduce the sentence meanings by composing smaller parts. To learn a well-defined semantics, semantic parsers must recognize small parts, which are semantic mappings between NL and semantic tokens. Attentions in recent neural models are usually explained as one-on-one semantic mappings. However, attention weights with end-to-end training are shown only weakly correlated with human-labeled mappings. Despite the usefulness, supervised mappings are expensive.