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REVIEW 3 major objections 4 minor 114 references

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

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that LLM dependency splits into two distinct, measurable dimensions: reliance on the model as a cognitive tool and attachment to it as a companion-like presence.

desk verdict A useful 12-item LLM dependency scale, but the CFA is not truly out-of-sample; treat the two-factor structure as promising rather than confirmed. read the letter →

arxiv 2506.06874 v4 pith:UWESRYKK submitted 2025-06-07 cs.HC cs.AI

Ala Yankouskaya , Areej B. Babiker , Syeda W. F. Rizvi , Sameha Alshakhsi , Magnus Liebherr , Raian Ali This is my paper
classification cs.HCcs.AI
keywords largelanguagemodelsdependencyinstrumentalrelationshipparasocialbondingscalevalidationfactoranalysiscognitiveoffloading
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to measure a form of dependency on large language models that is not captured by classic addiction questionnaires. It argues that people relate to LLMs in two psychologically distinct ways: as cognitive tools they lean on for tasks and decisions, and as companion-like presences they bond with emotionally. To make that distinction measurable, it develops and validates a 12-item questionnaire whose six instrumental items and six relationship items form two separate factors. The authors report that the two-factor structure fits the data well, that the subscales are internally consistent and discriminable, and that they relate differently to trust, internet addiction, and attitudes toward AI. The paper also argues that dependency on LLMs should not automatically be pathologized, since the two dimensions capture adaptive as well as potentially problematic reliance.

What carries the argument

The carrier of the argument is the questionnaire itself: 26 initial items generated from the authors' theoretical model were reduced by monotonicity checks and exploratory factor analysis to the 12 retained items. The decisive machinery is the split-sample factor-analytic procedure, in which a random half of the data is used to explore the structure and the other half is used to confirm it, with a network-psychometrics cross-check supporting the two clusters and discriminant-validity tests distinguishing the factors from a one-factor alternative. The two factors are the Instrumental Dependency and Relationship Dependency subscales, each with six items on a six-point Likert format.

What would settle it

Give the final 12 items to a fresh, preregistered sample and fit the two-factor model with no cross-loadings; if the fit indices fall below conventional thresholds or a one-factor model fits equally well, the claimed structure is not stable.

Watch

Extended reading notes

Core claim

The central claim is that LLM dependency is a two-dimensional construct rather than a single behavioural addiction: Instrumental Dependency (six items) captures reliance on the model for decision support, cognitive offloading, and task reward, while Relationship Dependency (six items) captures parasocial bonds, perceived companionship, and social substitution. Using split-sample exploratory and confirmatory factor analysis on 526 UK adults, the paper reports acceptable-to-good fit for the two-factor model, high internal consistency ($\alpha = .84$ and $.91$ for the two subscales), discriminant validity indicated by average variance extracted exceeding the squared factor correlation and by an HTMT ratio of $0.589$, and external validation showing that the subscales have different correlates. The paper's own framing is that existing tools built on DSM-5-style addiction symptoms miss what is specific to LLM interaction, and that a scale grounded in self-specificity, flow, parasocial interaction, and cognitive offloading captures the phenomenon more faithfully.

Load-bearing premise

The load-bearing premise is that the split into instrumental and relationship dependency is real and not an artifact of choosing which items to keep after looking at the data, since the exploratory analysis on the full dataset shaped the item set later confirmed on a split half.

Editorial extensions

If this is right

  • Researchers get a short, freely usable 12-item scale that separates cognitive reliance on LLMs from emotional bonding with them.
  • Studies can test whether the two dimensions have different predictors and outcomes instead of treating LLM dependence as one thing.
  • The item-level mapping in the appendix allows targeted work on specific behaviours such as social substitution, self-disclosure, and decision-making unease.
  • The results suggest that classic addiction-based questionnaires may miss LLM-specific dependency, because several items resembling traditional addiction symptoms were dropped during validation.
  • The scale can support responsible-AI design and digital-literacy efforts by identifying which type of dependence is present in a user.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because items refer to the respondent's self-selected primary LLM, the scale is likely transferable to future generative-AI systems and to a user's dominant chatbot, though that transfer is not directly tested.
  • The two-factor split implies that interventions may need different mechanisms: reducing instrumental dependency might target cognitive offloading and automation bias, while reducing relationship dependency might target parasocial bonding and social substitution.
  • A natural next step is to test whether the dropped items resembling classical addiction symptoms perform differently in clinical or heavy-user samples, which would clarify whether LLM-specific dependency truly lies outside existing addiction frameworks.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper develops and validates LLM-D12, a 12-item self-report scale intended to measure two dimensions of dependency on large language models: Instrumental Dependency (six items) and Relationship Dependency (six items). Items were generated from the authors' prior theoretical framework, refined through pilot testing, and administered to a UK sample (N=526). The authors report a two-factor structure supported by split-sample exploratory and confirmatory factor analyses, network psychometrics, good internal consistency, discriminant validity, and external validation against trust, internet addiction, AI attitudes, and need for cognition. The paper argues that LLM dependency is qualitatively different from classic behavioral addictions and that the new scale captures this distinction.

Significance. If the validation evidence were secure, LLM-D12 would be a practically useful tool for research on human-LLM interaction, filling a gap left by scales that import DSM-based addiction symptoms. The authors provide open data, a clear item-to-construct mapping in Appendix 2, and a worked scoring procedure, all of which are commendable. The conceptual distinction between instrumental and relational dependency is theoretically motivated and plausible. However, the central claim of cross-validation is weakened by a methodological inconsistency in the item-selection procedure, so the scale's status as a validated instrument is not yet established.

major comments (3)
  1. [§2.4.4 and §3.3] The claim that the CFA in §3.4 is an independent confirmation is undermined by the description of the initial EFA in §2.4.4: 'Initially, EFA was conducted on the full dataset... Items with factor loadings of 0.50 or higher were retained.' Because this full-sample analysis informed which items were removed (e.g., empowers_me, more_work_without, additional_brain, improves_work in Table 3), the subsequent 'confirmatory' half (N=263) contributed to item selection. The fit indices in §3.4 (CFI=0.958, TLI=0.947, RMSEA=0.076, SRMR=0.046) are therefore likely optimistic, and the central claim of a confirmed two-factor structure on an independent sample is not secure.
  2. [§2.4.3 and Table 2] The monotonicity check in §2.4.3 was performed on the full sample (N=526) and led to the removal of seven items before the split-sample EFA. Thus, even under the more favorable reading of §3.3, the CFA sample influenced item selection through the monotonicity screening. This is a second channel by which the 'independent' half is not truly independent, compounding the concern raised about the full-sample EFA.
  3. [§3.5.2] The discriminant validity evidence is borderline: the Instrumental Dependency AVE is 0.504 and the squared inter-construct correlation is 0.503, so the Fornell-Larcker criterion barely passes. Given that the two-factor structure itself is not confirmed on an independent sample, the discriminant validity claim is also not on firm ground.
minor comments (4)
  1. [§2.2] The text says 'data were collected from 646 individuals' but §2.4.1 reports 532 after preprocessing and 526 after exclusions; the discrepancy should be explained clearly.
  2. [§2.4.4] The phrase 'using the set of 19 items from the monotonicity check' in §3.3 is inconsistent with the description in §2.4.4 of a full-data EFA followed by item removal; the authors should clarify which item set was used at each stage.
  3. [Throughout] There are several typographical errors, including 'parasocial bounding' (should be 'bonding' in §1 and the Additional Keywords), 'TParticipants' (§2.2), and 'Gthat partioogle Nest' (§2.2). These should be corrected.
  4. [§3.4] The RMSEA of 0.076 exceeds the 0.07 cutoff cited by the authors, and the 90% CI upper bound (0.092) is above 0.08; the claim of 'good overall fit' should be tempered to 'acceptable' or 'moderate fit.'

Circularity Check

1 steps flagged · score 5.0 of 10

CFA is not a genuinely out-of-sample test because item selection used the full dataset, including the CFA half.

  1. fitted input called prediction [Section 2.4.4 (Exploratory Factor Analysis); cf. Sections 3.2-3.4]
    "Initially, EFA was conducted on the full dataset to gain an understanding of the underlying structure and item behaviour. Items with factor loadings of 0.50 or higher were retained for further analysis, while those falling below this threshold, as well as items that failed to meet the criteria set out in the data quality check procedure, were excluded."

    The CFA is presented as testing the factor structure on an independent sample (N=263) and the fit indices in Section 3.4 are offered as confirmation. But the item pool entering the split-half EFA and CFA was already reduced using a full-dataset EFA, meaning the CFA half contributed to which items survived. In addition, the monotonicity screening that produced the 19-item pool was performed on the full 526-participant dataset (Sections 2.4.3 and 3.2), so the CFA half also influenced that screening. The 'predictive' CFA is therefore not an out-of-sample validation; the good fit largely re-describes a structure selected using the same observations. The central claim that the two-factor structure was confirmed on an independent sample is compromised.

full rationale

The paper's main contribution is a new 12-item scale, and its central validation claim is that a split-sample EFA/CFA confirmed a two-factor structure. That claim is weakened by an internal methodological inconsistency: Section 2.4.4 states that an initial EFA was conducted on the full dataset and that items with loadings below 0.50 were excluded, while Section 3.3 describes the EFA as run on a randomly selected half using items that passed the monotonicity check. Under either reading, the CFA half influenced item selection: if the full-data EFA guided item removal, the 'independent' CFA sample is not independent; if only the monotonicity check was full-sample, that screening still used data from the CFA half. The CFA fit indices (CFI=0.958, TLI=0.947, RMSEA=0.076, SRMR=0.046) are thus likely inflated and do not provide the clean confirmatory evidence claimed. The paper does contain independent content: external validation with established measures (internet addiction, attitudes toward AI, need for cognition, trustworthiness of LLM) was not used to fit the factor solution, and the split-half EFA itself provides some structural evidence. The reliance on the authors' prior theoretical paper [19] for item generation is a self-citation, but the scale could have failed psychometrically, so that alone is not definitional circularity. The main circularity is the in-sample item selection being presented as an independent prediction, which is partial rather than total.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the authors' prior theoretical model of LLM dependency, standard psychometric assumptions, and the generalizability of a UK Prolific sample. No new entities are introduced, but several hand-chosen thresholds influence which items survived to the final scale.

free parameters (5)
  • Factor loading retention threshold = 0.50
    Items with loadings below 0.50 were removed from the scale. This threshold was chosen by hand, affects which items survived, and is not derived from data (Section 2.4.4, Table 3).
  • Monotonicity violation criteria = Not specified numerically beyond percent violated and zmax
    Seven items were removed for violating the monotonicity assumption based on thresholds in the mokken package. The specific cutoff values are not stated, so the removal rule is underdefined (Section 3.2).
  • Residual correlation cutoff for item removal = Not specified; residuals of 0.26, 0.15, 0.14 etc. were cited
    Items were removed because of residual correlations, but no formal cutoff is given, leaving the decision post hoc (Section 3.3).
  • Number of factors retained = 2
    Parallel analysis suggested 3 factors, but a 2-factor solution was retained after the 3-factor model showed poor fit. This is a model selection choice made on the same data (Section 3.3).
  • EBICglasso hyperparameter gamma = 0.5
    The default gamma was selected for the network analysis, influencing the sparsity and communities (Section 2.4.4).
assumptions (4)
  • domain assumption The theoretical model of LLM dependency as instrumental and relational (from Yankouskaya et al. [19]) is the correct foundation for item generation.
    All items were written to match the authors' prior theory, so the factor structure is partly built into the items from the start (Section 2.1).
  • domain assumption Self-report responses on a 6-point Likert scale accurately reflect actual psychological dependency on LLMs.
    No objective behavioral measure of dependency was collected; the entire scale is based on participant self-report (Section 2.3).
  • domain assumption The UK Prolific sample is generalizable to broader LLM user populations.
    The authors acknowledge the sample is a convenience sample and culturally homogeneous, which limits generalization (Section 4.6).
  • standard math Standard psychometric assumptions hold: approximate normality, missing at random, and no severe common method bias.
    These assumptions underlie the use of maximum likelihood CFA, FIML, and the Harman single-factor test (Sections 2.4.5 and 3.5.3).

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Cite this review

Pith. "Pith review of LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models." pith.science (2026). https://pith.science/paper/UWESRYKK

@misc{pith2026250606874,
  author       = {Pith},
  title        = {Pith review of: LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UWESRYKK}},
  note         = {Machine review of arXiv:2506.06874}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2506.06874 by the authors.

Figure 1
Figure 1. Network Structure of LLM Dependency scale after EFA. The plot displays partial correlation structure among questionnaire items, identifying two distinct item communities: one representing instrumental/task dependency (blue nodes) and the other capturing relationship dependency (red nodes). Solid edges denote stronger within-community associations, while dashed edges indicate inter￾community connections 3.4 Confirmat… view at source ↗
Figure 2
Figure 2. Associations between Instrumental and Relationship Dependency and [PITH_FULL_IMAGE:figures/full_fig_p017_2.png] view at source ↗
Figure 3
Figure 3. Workflow and main results of external validation [PITH_FULL_IMAGE:figures/full_fig_p018_3.png] view at source ↗

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.