Understanding and classifying query intents can improve retrieval effectiveness by helping align search results with the motivations behind user queries. However, existing intent taxonomies are typically derived from system log data and capture mostly isolated information needs, while the broader task context often remains unaddressed. This limitation becomes increasingly relevant as interactions with Large Language Models (LLMs) expand user expectations from simple query answering toward comprehensive task support, for example, with purchasing decisions or in travel planning. At the same time, current LLMs still struggle to fully interpret complex and multifaceted tasks. To address this gap, we argue for a stronger task-based perspective on query intent. Drawing on a grounded-theory-based interview study with airport information clerks, we present a taxonomy of task-based information request intents that bridges the gap between traditional query-focused approaches and the emerging demands of AI-driven task-oriented search.

翻译：理解并分类查询意图可通过使搜索结果与用户查询背后的动机相匹配来提高检索效果。然而，现有的意图分类法通常源自系统日志数据，主要捕捉孤立的信息需求，而更广泛的任务背景往往未被考虑。随着与大语言模型（LLMs）的交互将用户期望从简单的查询应答扩展到全面的任务支持（例如在购买决策或旅行规划中），这一局限性日益凸显。与此同时，当前的大语言模型仍难以完全解析复杂且多方面的任务。为弥补这一不足，我们主张采用更强的基于任务的视角来看待查询意图。基于一项对机场信息咨询员进行的扎根理论访谈研究，我们提出了一种基于任务的信息请求意图分类法，旨在弥合传统以查询为中心的方法与人工智能驱动的任务导向搜索新兴需求之间的差距。