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

arXiv: https://arxiv.org/abs/2506.06874 · HTML: https://mlsanity.com/paper/2506.06874/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 116 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [TrustLLM: Trustworthiness in Large Language Models](https://mlsanity.com/paper/2401.05561/) (2024)
- [Evaluating Large Language Models in Theory of Mind Tasks](https://mlsanity.com/paper/2302.02083/) (2023)
- [The opportunities and risks of large language models in mental health](https://mlsanity.com/paper/2403.14814/) (2024)
- [Trust in AI and Its Role in the Acceptance of AI Technologies](https://mlsanity.com/paper/2203.12687/) (2022)
- [Human-AI Interactions in Public Sector Decision-Making: "Automation Bias" and "Selective Adherence" to Algorithmic Advice](https://mlsanity.com/paper/2103.02381/) (2021)
- [Inducing anxiety in large language models can induce bias](https://mlsanity.com/paper/2304.11111/) (2023)
- [Longitudinal Study on Social and Emotional Use of AI Conversational Agent](https://mlsanity.com/paper/2504.14112/) (2025)
- [Generative Artificial Intelligence: Evolving Technology, Growing Societal Impact, and Opportunities for Information Systems Research](https://mlsanity.com/paper/2503.05770/) (2025)
- [Network Psychometrics](https://mlsanity.com/paper/1609.02818/) (2016)

## Built on by (most cited first, 3 of 3)

- [Brief chatbot interactions produce lasting changes in human moral values](https://mlsanity.com/paper/2604.21430/) (2026)
- ["Oops! ChatGPT is Temporarily Unavailable!": A Diary Study on Knowledge Workers' Experiences of LLM Withdrawal](https://mlsanity.com/paper/2603.26099/) (2026)
- [Mapping generative AI use in the human brain: divergent neural, academic, and mental health profiles of functional versus socio emotional AI use](https://mlsanity.com/paper/2604.08594/) (2026)

## Abstract (arXiv)

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.