Developing and Validating the Spanish Version of the Large Language Models Dependency Scale (LLM-D12-SP)

• URL: http://arxiv.org/abs/2607.22041v1
• Date: Fri, 24 Jul 2026 07:09:56 GMT
• Title: Developing and Validating the Spanish Version of the Large Language Models Dependency Scale (LLM-D12-SP)
• Abstract summary: There is a growing need for reliable and culturally validated instruments to assess psychological dependency on large language models (LLMs)<n>The present study reports the first validation of the Spanish version of the Large Language Model Dependency Scale (LLM-D12-SP)
• Score: 1.6375706215572936
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is a growing need for reliable and culturally validated instruments to assess psychological dependency on large language models (LLMs), particularly as LLMs are increasingly used for task execution, decision-making, and communication in organizational and work-related settings. This need is especially relevant for Spanish-speaking populations, where LLM adoption is rapidly expanding, yet validated psychometric tools remain scarce. The present study reports the first validation of the Spanish version of the Large Language Model Dependency Scale (LLM-D12-SP), extending prior validations conducted in English- and Arabic-speaking samples. The LLM-D12 is a two-dimensional instrument assessing Instrumental Dependency (reliance on LLMs for performing tasks and supporting decisions) and Relationship Dependency (psychological reliance on LLMs for companionship and social interaction). A total of 386 Spanish-speaking participants (M = 28.0 years, SD = 6.1; 55% male) completed the LLM-D12-SP. Confirmatory factor analysis supported the original two-factor structure. The scale demonstrated good internal consistency (Cronbach's alpha = 0.89 total; 0.86 Instrumental; 0.85 Relationship). Discriminant validity analyses indicated that the two subscales represent related but distinct constructs. External validation showed that both dependency dimensions were positively associated with internet addiction and perceived trustworthiness of LLMs, while showing weak or no association with need for cognition. Together with prior English and Arabic validations, these findings establish cross-linguistic support for the scale's structure and provide a psychometrically sound tool for investigating psychological aspects of LLM use in organizational contexts.

Related papers

• Do Assessment Instruments Measure the Same Thing for Humans and LLMs? A Latent Structure Analysis [0.0]
  This study examines whether similarity in the latent structure of responses to an assessment holds for large language models (LLMs)<n>We find systematic differences between human and LLM factor structures, showing evidence that the analyzed assessments may not measure the same constructs for humans and LLMs.<n>These findings call into question the validity of evaluation practices that use educational assessments to make claims about AI capabilities.
  arXiv  Detail & Related papers  (2026-08-16T08:52:58Z)
• The Emergence of Abstract Thought in Large Language Models Beyond Any Language [95.50197866832772]
  Large language models (LLMs) function effectively across a diverse range of languages.<n>Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts.<n>Recent results show strong multilingual performance, even surpassing English performance on specific tasks in other languages.
  arXiv  Detail & Related papers  (2025-06-11T16:00:54Z)
• LLM-D12: A Dual-Dimensional Scale of Instrumental and Relational Dependencies on Large Language Models [0.24629531282150877]
  There is growing interest in understanding how people interact with large language models (LLMs)<n>We developed and validated a new 12-item questionnaire to measure LLM dependency, referred to as LLM-D12.<n>The scale was based on the authors' prior theoretical work, with items developed accordingly and responses collected from 526 participants in the UK.
  arXiv  Detail & Related papers  (2025-06-07T17:42:21Z)
• When Less Language is More: Language-Reasoning Disentanglement Makes LLMs Better Multilingual Reasoners [111.50503126693444]
  We show that language-specific ablation consistently boosts multilingual reasoning performance.<n>Compared to post-training, our training-free ablation achieves comparable or superior results with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-05-21T08:35:05Z)
• Understanding LLMs' Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From [73.76577052090695]
  We evaluate the cross-lingual context retrieval of over 40 large language models (LLMs) across 12 languages.<n>Post-trained open LLMs show strong cross-lingual context retrieval ability, comparable to closed-source LLMs.<n>Our results indicate that larger-scale pretraining cannot improve the xMRC performance.
  arXiv  Detail & Related papers  (2025-04-15T06:35:27Z)
• Disparities in LLM Reasoning Accuracy and Explanations: A Case Study on African American English [66.97110551643722]
  We investigate dialectal disparities in Large Language Models (LLMs) reasoning tasks.<n>We find that LLMs produce less accurate responses and simpler reasoning chains and explanations for AAE inputs.<n>These findings highlight systematic differences in how LLMs process and reason about different language varieties.
  arXiv  Detail & Related papers  (2025-03-06T05:15:34Z)
• ExpliCa: Evaluating Explicit Causal Reasoning in Large Language Models [75.05436691700572]
  We introduce ExpliCa, a new dataset for evaluating Large Language Models (LLMs) in explicit causal reasoning.<n>We tested seven commercial and open-source LLMs on ExpliCa through prompting and perplexity-based metrics.<n>Surprisingly, models tend to confound temporal relations with causal ones, and their performance is also strongly influenced by the linguistic order of the events.
  arXiv  Detail & Related papers  (2025-02-21T14:23:14Z)
• Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence [49.60849499134362]
  This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning)<n>We introduce a neurolinguistic approach, utilizing a novel method that combines minimal pair and diagnostic probing to analyze activation patterns across model layers.<n>We found: (1) Psycholinguistic and neurolinguistic methods reveal that language performance and competence are distinct; (2) Direct probability measurement may not accurately assess linguistic competence; and (3) Instruction tuning won't change much competence but improve performance.
  arXiv  Detail & Related papers  (2024-11-12T04:16:44Z)
• LMLPA: Language Model Linguistic Personality Assessment [11.599282127259736]
  Large Language Models (LLMs) are increasingly used in everyday life and research. measuring the personality of a given LLM is currently a challenge. This paper introduces the Language Model Linguistic Personality Assessment (LMLPA), a system designed to evaluate the linguistic personalities of LLMs.
  arXiv  Detail & Related papers  (2024-10-23T07:48:51Z)
• MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models [23.384966485398184]
  This paper introduces a comprehensive investigation of Multilingual Confidence estimation (MlingConf) on Large Language Models (LLMs) The benchmark comprises four meticulously checked and human-evaluated high-quality multilingual datasets for LA tasks and one for the LS task tailored to specific social, cultural, and geographical contexts of a language. Experiments reveal that on LA tasks English exhibits notable linguistic dominance in confidence estimations than other languages, while on LS tasks, using question-related language to prompt LLMs demonstrates better linguistic dominance in multilingual confidence estimations.
  arXiv  Detail & Related papers  (2024-10-16T11:46:55Z)
• The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights [108.40766216456413]
  We propose a question alignment framework to bridge the gap between large language models' English and non-English performance. Experiment results show it can boost multilingual performance across diverse reasoning scenarios, model families, and sizes. We analyze representation space, generated response and data scales, and reveal how question translation training strengthens language alignment within LLMs.
  arXiv  Detail & Related papers  (2024-05-02T14:49:50Z)
• MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models [36.33453112932689]
  This paper introduces a comprehensive investigation of Multilingual Confidence estimation (MlingConf) on Large Language Models (LLMs)<n>The benchmark comprises four meticulously checked and human-evaluated high-quality multilingual datasets for LA tasks and one for the LS task tailored to specific social, cultural, and geographical contexts of a language.<n>Experiments reveal that on LA tasks English exhibits notable linguistic dominance in confidence estimations than other languages, while on LS tasks, using question-related language to prompt LLMs demonstrates better linguistic dominance in multilingual confidence estimations.
  arXiv  Detail & Related papers  (2024-02-21T08:20:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.