Year: 2026
Type: conference-paper
Abstract: Recent advancements in Large Language Models (LLMs) have empowered home assistants with natural language interaction capabilities.However, current assistants overlook the progressive omission that occurs in human dialogue as shared context accumulates, leading to more elliptical expressions for efficient communication.Thus, current assistants still struggle to interpret such elliptical expressions accurately, which limits their effectiveness in realworld applications.In practical smart home scenarios, assistants face two major challenges caused by elliptical commands: (1) referential ambiguity caused by different environmental expectations among multiple users; and (2) intention ambiguity resulting from user preferences that evolve over time or change with the environment.To address these challenges, we introduce PEC-Home, the first simulated home dataset specifically designed for interpreting progressively elliptical commands in smart homes.Extensive experiments on various LLMs, including GPT-4o, show that existing home assistants struggle to execute userintended operations based solely on elliptical commands.Even when equipped with tools for storing and retrieving user dialogue history, execution accuracy remains below that achieved with complete commands.
Institutions: Beijing Institute of Technology, Beihang University, Baidu (China)
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G2563629645
OpenAlex ID: W7166890336