Rules, Resources, and Restrictions: A Taxonomy of Task-Based Information Request Intents

2026-01-19

AI Summary

This study addresses the limitations of existing query intent classification approaches, which predominantly rely on system logs and neglect user task context, thereby falling short in supporting the nuanced understanding required for complex tasks in the era of large language models. Drawing on grounded theory, the authors conduct qualitative interviews with airport information officers and integrate task analysis with intent modeling to develop the first query intent classification framework that explicitly incorporates task context. Moving beyond traditional models that treat information needs in isolation, the proposed framework introduces a three-dimensional taxonomy of task-oriented information requests—encompassing rules, resources, and constraints—offering a novel paradigm for task-driven retrieval and interaction with large language models.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Intelligent Query Processing
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• query intent
• task-based search
• information needs
• Large Language Models
• intent taxonomy

Innovation

• task-based intent
• information request taxonomy
• grounded theory
• large language models
• search intent classification