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

• URL: http://arxiv.org/abs/2502.12923v2
• Date: Fri, 21 Mar 2025 08:23:56 GMT
• Title: On-Device LLMs for Home Assistant: Dual Role in Intent Detection and Response Generation
• Abstract summary: This paper investigates whether Large Language Models (LLMs) can perform the twofold task of slot and intent detection and natural language response generation for a smart home assistant.<n>We fine-tune LLMs to produce both action calls and text responses.<n>Our experiments show that 16-bit and 8-bit quantized variants preserve high accuracy on slot and intent detection and maintain a strong semantic coherence in generated text.
• Score: 5.500769111819106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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