ArticlePloS one2026
Research on the development of an automated system for psychology questionnaire generation based on large language models.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
9 authors.
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Abstract
This study reimagined the psychology questionnaire development process using large language model ((LLM) technology, aiming to overcome the protracted preparation cycles and significant human bias inherent in traditional scale development. We developed a specialized fine-tuning scheme for a corpus of 169 professional psychological questionnaires. By integrating instruction fine-tuning with human feedback reinforcement, we significantly enhanced the adaptability of the Qwen-2.5 and GLM-4 models for demanding professional psychological assessment tasks. The optimized models demonstrated remarkable gains across key dimensions: text generation quality (BLEU-4 increased by 0.05, ROUGE-L by 0.057), scientific rigor (logical consistency improved by 28.6%), and cultural adaptability (achieving over 85% accuracy in cross-regional expression conversion). This research solidly supports the feasibility of leveraging LLM technology to drive research paradigm transformation in psychology, offering crucial methodological support for developing efficient, intelligent psychological measurement tools.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.