在手机级设备上用大模型实现指令识别与自然回复，无需云端支持。
On-Device LLMs for Home Assistant: Dual Role in Intent Detection and Response Generation
- 用合成数据微调大模型，同时完成指令解析和文本生成。
- 8位量化模型保持高准确率，4位模型生成流畅但分类准确率下降。
- 适合资源受限的智能家居场景，尤其适合单次指令响应。
本文研究大型语言模型（LLMs）在仅具备CPU的边缘设备上，能否通过微调合成但领域代表性数据，完成智能家居助手的双重任务：（i）槽位与意图检测，（ii）自然语言响应生成。我们微调模型以输出JSON动作调用与文本响应。实验表明，16位与8位量化模型在槽位与意图检测上保持高准确率，生成文本语义连贯性强；而4位模型虽维持生成流畅性，但在设备服务分类准确率上出现明显下降。对噪声人类输入及跨领域意图的进一步评估显示，模型具有较好泛化能力，准确率维持在80%–86%。尽管平均推理时间每查询达5–6秒（适用于单次指令，但多轮对话表现不佳），结果证实，仅靠边缘设备上的大模型即可有效统一命令理解与灵活回应生成，无需依赖专用硬件。
原文摘要 · Abstract (English)
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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