让智能助手理解用户越来越省略的指令，提升真实场景下的交互准确率。
PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes
- 构建首个针对智能家庭中逐步省略指令的仿真数据集。
- 实验表明现有大模型在省略指令下执行准确率低于完整指令。
- 适用于提升多用户、动态环境中的智能助手理解能力。
大型语言模型（LLMs）的进步使家居助手具备了自然语言交互能力。然而，当前助手忽视了人类对话中因共享上下文累积而产生的逐步省略现象，导致对省略性指令的理解不准确，限制了其在真实场景中的应用效果。在实际智能家居环境中，助手面临两大挑战：(1) 多用户间因环境预期差异引发的指代歧义；(2) 用户偏好随时间或环境变化导致的意图歧义。为此，我们提出 PEC-Home，首个专为解读智能家庭中逐步省略指令而设计的仿真数据集。在多种大模型（包括 GPT-4o）上的广泛实验表明，仅凭省略指令，现有助手难以正确执行用户意图操作。即使使用工具存储和检索对话历史，执行准确率仍低于完整指令场景。
原文摘要 · Abstract (English)
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 real-world 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 user-intended 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.}.
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