PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes

2026-06-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Internet of Things (IoT) & Connected Devices · Depth: Expert, long

Summary

PEC- is the first simulated dataset designed to address the interpretation of progressively elliptical commands in smart home environments, a significant challenge for current Large Language Models (LLMs). This dataset, comprising 1,780 dialogues from 1,424 unique personas, simulates how human commands naturally become shorter and less explicit over long-term interactions. It specifically tackles two core ambiguities: referential ambiguity arising from multi-user preferences and intention ambiguity from dynamic user preferences. PEC- includes a virtual environment with 12 device types and over 50 distinct methods, totaling more than 350 personalized methods across various rooms. Extensive experiments on 10 distinct LLMs, including GPT-4o, using zero-shot prompting, in-context learning, and Retrieval-Augmented Generation (RAG), reveal substantial performance drops when interpreting elliptical commands compared to complete ones, even with these enhancements. The dataset and code are publicly available.

Key takeaway

For AI Scientists and Machine Learning Engineers developing smart home assistants, you must account for progressively elliptical user commands. Your current LLM-based solutions, even with RAG or fine-tuning, will likely fail to reliably interpret these natural, context-dependent expressions. Prioritize developing models capable of handling referential and intention ambiguities arising from multi-user and dynamic preferences to ensure intuitive, efficient user interaction.

Key insights

LLMs struggle with progressively elliptical smart home commands, necessitating specialized datasets and interpretation methods.

Principles

• Human dialogue naturally omits context over time.
• Multi-user preferences create referential ambiguity.
• Dynamic user preferences cause intention ambiguity.

Method

PEC- dataset generation involves creating a virtual smart home environment, generating function-call operations, and using GPT-4o to produce progressively elliptical natural language commands across four defined levels.

In practice

• Develop LLM agents for multi-user contexts.
• Design LLMs to adapt to dynamic preferences.
• Test LLMs with progressively elliptical commands.

Topics

• Smart Home Automation
• Large Language Models
• Elliptical Commands
• Dialogue Context
• Dataset Creation
• Referential Ambiguity

Code references

• BITHLP/PEC-

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.