Leveraging Large Language Models for enhanced personalised user experience in Smart Homes

TL;DR

This paper introduces a novel smart home system that uses Large Language Models and user preferences to enhance personalization, demonstrating significant improvements in comfort, safety, and efficiency over traditional routines.

Contribution

It presents an innovative architecture integrating LLMs and user preferences for smarter, more intuitive home automation, with real-world implementation and performance evaluation.

Findings

Up to 52.3% increase in user satisfaction scores

Processing time reduced by 35.6% on Starling 7B Alpha LLM

Performance surpasses larger models by 26.4% with faster processing

Abstract

Smart home automation systems aim to improve the comfort and convenience of users in their living environment. However, adapting automation to user needs remains a challenge. Indeed, many systems still rely on hand-crafted routines for each smart object.This paper presents an original smart home architecture leveraging Large Language Models (LLMs) and user preferences to push the boundaries of personalisation and intuitiveness in the home environment.This article explores a human-centred approach that uses the general knowledge provided by LLMs to learn and facilitate interactions with the environment.The advantages of the proposed model are demonstrated on a set of scenarios, as well as a comparative analysis with various LLM implementations. Some metrics are assessed to determine the system's ability to maintain comfort, safety, and user preferences. The paper details the approach to real-world implementation and evaluation.The proposed approach of using preferences shows up to 52.3% increase in average grade, and with an average processing time reduced by 35.6% on Starling 7B Alpha LLM. In addition, performance is 26.4% better than the results of the larger models without preferences, with processing time almost 20 times faster.