LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models

2025-06-07

AI Summary

Existing LLM dependency assessment tools mechanically adopt behavioral addiction frameworks, neglecting the complexity of human–LLM interaction and its contextual sensitivity. Method: We propose the first theory-driven, dual-dimensional model—distinguishing instrumental dependence from relational dependence—and develop the 12-item LLM-D12 scale. Based on data from 526 UK participants, we conduct exploratory and confirmatory factor analyses (EFA/CFA), split-half cross-validation, and comprehensive psychometric evaluation—including internal consistency (α > 0.90), discriminant validity, and criterion-related validity. Contribution/Results: The LLM-D12 demonstrates a robust two-factor structure aligned with theoretical expectations. It transcends pathological assumptions by offering the first standardized, non-addiction-oriented, context-sensitive measurement tool for human–LLM interaction—enabling nuanced empirical investigation of dependence beyond clinical pathology.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

There is growing interest in understanding how people interact with large language models (LLMs) and whether such models elicit dependency or even addictive behaviour. Validated tools to assess the extent to which individuals may become dependent on LLMs are scarce and primarily build on classic behavioral addiction symptoms, adapted to the context of LLM use. We view this as a conceptual limitation, as the LLM-human relationship is more nuanced and warrants a fresh and distinct perspective. To address this gap, we developed and validated a new 12-item questionnaire to measure LLM dependency, referred to as LLM-D12. The scale was based on the authors' prior theoretical work, with items developed accordingly and responses collected from 526 participants in the UK. Exploratory and confirmatory factor analyses, performed on separate halves of the total sample using a split-sample approach, supported a two-factor structure: Instrumental Dependency (six items) and Relationship Dependency (six items). Instrumental Dependency reflects the extent to which individuals rely on LLMs to support or collaborate in decision-making and cognitive tasks. Relationship Dependency captures the tendency to perceive LLMs as socially meaningful, sentient, or companion-like entities. The two-factor structure demonstrated excellent internal consistency and clear discriminant validity. External validation confirmed both the conceptual foundation and the distinction between the two subscales. The psychometric properties and structure of our LLM-D12 scale were interpreted in light of the emerging view that dependency on LLMs does not necessarily indicate dysfunction but may still reflect reliance levels that could become problematic in certain contexts.

Problem

• Assessing dependency on LLMs lacks validated tools
• Existing tools overlook nuanced LLM-human relationships
• Measuring instrumental and relational LLM dependencies separately

Innovation

• Developed a 12-item LLM dependency questionnaire
• Two-factor structure: Instrumental and Relationship Dependency
• Validated with 526 UK participants' data