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arXiv · 2502.12923

On-Device LLMs for Home Assistant: Dual Role in Intent Detection and Response Generation

Rune Birkmose·Nathan Mørkeberg Reece·Esben Hofstedt Norvin·Johannes Bjerva·Mike Zhang
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Abstract

This paper investigates whether Large Language Models (LLMs), fine-tuned on synthetic but domain-representative data, can perform the twofold task of (i) slot and intent detection and (ii) natural language response generation for a smart home assistant, while running solely on resource-limited, CPU-only edge hardware. We fine-tune LLMs to produce both JSON action calls and text responses. Our experiments show that 16-bit and 8-bit quantized variants preserve high accuracy on slot and intent detection and maintain strong semantic coherence in generated text, while the 4-bit model, while retaining generative fluency, suffers a noticeable drop in device-service classification accuracy. Further evaluations on noisy human (non-synthetic) prompts and out-of-domain intents confirm the models' generalization ability, obtaining around 80--86\% accuracy. While the average inference time is 5--6 seconds per query -- acceptable for one-shot commands but suboptimal for multi-turn dialogue -- our results affirm that an on-device LLM can effectively unify command interpretation and flexible response generation for home automation without relying on specialized hardware.

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Rune Birkmose, Nathan Mørkeberg Reece, Esben Hofstedt Norvin, Johannes Bjerva, Mike Zhang. 2025-03-21. On-Device LLMs for Home Assistant: Dual Role in Intent Detection and Response Generation. https://arxiv.org/abs/2502.12923

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