---
title: "Developing and Validating the Spanish Version of the Large Language — paper by Bao et al. (2026)"
description: "The paper fills a gap in psychometric tools: no validated Spanish-language questionnaire existed for measuring how strongly people rely on… 49 references."
authors: ["Tran Gia Bao", "Mo El-Haj", "Sameha Al-Shakhsi", "Antonio Garcia-Cabot", "Raian Ali", "Ala Yankouskaya"]
year: 2026
venue: "arXiv:2607.22041 · Discover Psychology 2026"
source: "https://arxiv.org/abs/2607.22041"
doi: "10.21203/rs.3.rs-8940489/v1"
arxiv: "2607.22041"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/bao-2026-developing-validating-spanish-version-large/"
---
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# Developing and Validating the Spanish Version of the Large Language Models Dependency Scale (LLM-D12-SP)

**Authors:** Tran Gia Bao, [Mo El-Haj](https://md.research.elamur.ai/free/en/authors/mo-el-haj/), Sameha Al-Shakhsi, Antonio Garcia-Cabot, Raian Ali and Ala Yankouskaya · **Year:** 2026 · **Venue:** arXiv:2607.22041 · Discover Psychology 2026 · **Publisher:** [Springer Science+Business Media](https://md.research.elamur.ai/free/en/publishers/springer-science-business-media/) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.21203/rs.3.rs-8940489/v1) · [PDF](https://arxiv.org/pdf/2607.22041) · [DOI 10.21203/rs.3.rs-8940489/v1](https://doi.org/10.21203/rs.3.rs-8940489/v1) · [arXiv 2607.22041](https://arxiv.org/abs/2607.22041) · [OpenAlex](https://openalex.org/W7134176015)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
The paper fills a gap in psychometric tools: no validated Spanish-language questionnaire existed for measuring how strongly people rely on large language models. The authors translated the LLM-D12, a 12-item scale previously validated in English and Arabic, into Spanish and tested it as LLM-D12-SP. The scale splits reliance into two kinds: Instrumental Dependency, which covers leaning on LLMs for tasks and decisions, and Relationship Dependency, which covers treating them as companions or social partners. They surveyed 386 adults of Spanish origin living in Spain, recruited online with attention checks. Confirmatory factor analysis reproduced the original two-factor structure. Reliability was good, and the two subscales proved correlated yet separable. Both kinds of dependency rose with internet addiction and with trust in LLMs, but they barely tracked need for cognition. Across three languages, this gives evidence that the scale's structure holds cross-linguistically. It also gives Spanish-language researchers a tool that does not treat all reliance as one "problematic use" score.

## Key points
- The sample had 386 participants with a mean age of 28.0 (SD 6.1), and 55% were male. They were recruited via Prolific, surveyed in SurveyMonkey, and paid only if they passed embedded attention checks.
- The scale has two six-item subscales, Instrumental and Relationship Dependency. CFA supported this two-factor model.
- Cronbach's α was 0.89 for the full scale, 0.86 for Instrumental and 0.85 for Relationship.
- In external validation, both subscales correlated positively with internet addiction and perceived LLM trustworthiness, and weakly or not at all with need for cognition.
- The validation also included item-level and within-subscale network analyses. The dataset and analysis files are on OSF, and the study builds on earlier English and Arabic validations.

## Interesting ideas
- Earlier scales fold different reasons for heavy use into a single severity score; this one deliberately separates them into distinct dimensions, so different behaviours don't produce identical scores.
- Discriminant validity checks that two subscales are related but not redundant, which tests whether splitting a construct was justified.
- External validation pairs expected-positive correlates with a correlate expected to show little or no link (need for cognition), so the measure is also tested on what it should not track.
- The same instrument is re-validated across languages to show its structure is not an artifact of one language.
- Respondents who failed attention checks embedded in the survey were not paid, which gives a cheap quality gate for crowd data.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (answer): Score reader errors along separate axes, such as fact lookup versus inference versus pragmatics, so a single accuracy number doesn't hide which skill is actually failing.
- **I2** (learn): Check whether proposed error categories or repair-rule families really behave differently on data before keeping them as separate classes.
- **I3** (learn): Include negative-control features or tasks in evaluation, where a correct component should show no gain, to catch metrics that improve for spurious reasons.
- **I4** (tree): After UD dialect conversion, verify that parser behaviour on core relations stays stable across treebanks, as evidence that the conversion preserves structure.
- **I5** (learn): Plant known-answer probe sentences in LLM-teacher annotation batches and discard batches that fail them before they enter silver data.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Tran Gia Bao, Mo El-Haj, Sameha Al-Shakhsi, Antonio Garcia-Cabot, Raian Ali and Ala Yankouskaya. 2026. Developing and Validating the Spanish Version of the Large Language Models Dependency Scale (LLM-D12-SP). arXiv:2607.22041 · Discover Psychology 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{bao2026developing,
  title = {{Developing and Validating the Spanish Version of the Large Language Models Dependency Scale (LLM-D12-SP)}},
  author = {Tran Gia Bao, Mo El-Haj, Sameha Al-Shakhsi, Antonio Garcia-Cabot, Raian Ali and Ala Yankouskaya},
  year = {2026},
  doi = {10.21203/rs.3.rs-8940489/v1},
  eprint = {2607.22041},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.21203/rs.3.rs-8940489/v1}
}
```

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49 references, 1 of them with a card of ours. ↗ leads to the work.

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*Updated 24 October 2026*