Automated Item Neutralization for Non-Cognitive Scales: A Large Language Model Approach to Reducing Social-Desirability Bias
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Abstract (arXiv)
This study evaluates item neutralization assisted by the large language model (LLM) to reduce social desirability bias in personality assessment. GPT-o3 was used to rewrite the International Personality Item Pool Big Five Measure (IPIP-BFM-50), and 203 participants completed either the original or neutralized form along with the Marlowe-Crowne Social Desirability Scale. The results showed preserved reliability and a five-factor structure, with gains in Conscientiousness and declines in Agreeableness and Openness. The correlations with social desirability decreased for several items, but inconsistently. Configural invariance held, though metric and scalar invariance failed. Findings support AI neutralization as a potential but imperfect bias-reduction method.
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References · 7
- 2023Better Zero-Shot Reasoning with Role-Play Promptingcited by 223
- 2025The relationship between reasoning and performance in large language models--o3 (mini) thinks harder, not longercited by 41
- 2024Fine-tuning and Utilization Methods of Domain-specific LLMscited by 36
- 2024Large Language Models Show Human-like Social Desirability Biases in Survey Responsescited by 28
- 2023Prompt Sapper: A LLM-Empowered Production Tool for Building AI Chainscited by 24
- 2024GPT for Games: An Updated Scoping Review (2020-2024)cited by 10
- 2024Exploring Social Desirability Response Bias in Large Language Models: Evidence from GPT-4 Simulationscited by 5
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