@inproceedings{birkmose-etal-2025-device,
    title = "On-Device {LLM}s for Home Assistant: Dual Role in Intent Detection and Response Generation",
    author = "Birkmose, Rune  and
      Reece, Nathan M{\o}rkeberg  and
      Norvin, Esben Hofstedt  and
      Bjerva, Johannes  and
      Zhang, Mike",
    editor = "Bak, JinYeong  and
      Goot, Rob van der  and
      Jang, Hyeju  and
      Buaphet, Weerayut  and
      Ramponi, Alan  and
      Xu, Wei  and
      Ritter, Alan",
    booktitle = "Proceedings of the Tenth Workshop on Noisy and User-generated Text",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.wnut-1.7/",
    pages = "57--67",
    ISBN = "979-8-89176-232-9",
    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."
}