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

TL;DR

This paper introduces a new taxonomy of task-based information request intents, emphasizing the importance of understanding broader user tasks to improve search and AI-driven assistance, especially with LLMs.

Contribution

It presents a novel taxonomy based on grounded theory from interviews, bridging traditional query intent models and the needs of AI task support.

Findings

Developed a taxonomy of task-based request intents

Highlights the gap in existing intent taxonomies for complex tasks

Provides insights for improving LLM understanding of multifaceted user needs

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.