# "Get ready for a party": Exploring smarter smart spaces with help from large language models

arXiv: https://arxiv.org/abs/2303.14143 · HTML: https://mlsanity.com/paper/2303.14143/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [Scalable Multi Corpora Neural Language Models for ASR](https://mlsanity.com/paper/1907.01677/) (2019)

## Built on by (most cited first, 10 of 10)

- [Sasha: Creative Goal-Oriented Reasoning in Smart Homes with Large Language Models](https://mlsanity.com/paper/2305.09802/) (2023)
- [Thoughtful Things: Building Human-Centric Smart Devices with Small Language Models](https://mlsanity.com/paper/2405.03821/) (2024)
- [Leveraging Large Language Models for enhanced personalised user experience in Smart Homes](https://mlsanity.com/paper/2407.12024/) (2024)
- [Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings](https://mlsanity.com/paper/2512.25055/) (2025)
- [Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A Survey](https://mlsanity.com/paper/2412.14708/) (2024)
- [FactFlow: Automatic Fact Sheet Generation and Customization from Tabular Dataset via AI Chain Design & Implementation](https://mlsanity.com/paper/2502.17909/) (2025)
- [Towards Privacy-Preserving and Personalized Smart Homes via Tailored Small Language Models](https://mlsanity.com/paper/2507.08878/) (2025)
- [Proactive Rejection and Grounded Execution: A Dual-Stage Intent Analysis Paradigm for Safe and Efficient AIoT Smart Homes](https://mlsanity.com/paper/2603.16207/) (2026)
- [Intelligence of Things: A Spatial Context-Aware Control System for Smart Devices](https://mlsanity.com/paper/2504.13942/) (2025)
- [Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing](https://mlsanity.com/paper/2606.22496/) (2026)

## Abstract (arXiv)

The right response to someone who says "get ready for a party" is deeply influenced by meaning and context. For a smart home assistant (e.g., Google Home), the ideal response might be to survey the available devices in the home and change their state to create a festive atmosphere. Current practical systems cannot service such requests since they require the ability to (1) infer meaning behind an abstract statement and (2) map that inference to a concrete course of action appropriate for the context (e.g., changing the settings of specific devices). In this paper, we leverage the observation that recent task-agnostic large language models (LLMs) like GPT-3 embody a vast amount of cross-domain, sometimes unpredictable contextual knowledge that existing rule-based home assistant systems lack, which can make them powerful tools for inferring user intent and generating appropriate context-dependent responses during smart home interactions. We first explore the feasibility of a system that places an LLM at the center of command inference and action planning, showing that LLMs have the capacity to infer intent behind vague, context-dependent commands like "get ready for a party" and respond with concrete, machine-parseable instructions that can be used to control smart devices. We furthermore demonstrate a proof-of-concept implementation that puts an LLM in control of real devices, showing its ability to infer intent and change device state appropriately with no fine-tuning or task-specific training. Our work hints at the promise of LLM-driven systems for context-awareness in smart environments, motivating future research in this area.