MindShift: Analyzing Language Models Reactions to Psychological Prompts

📝 Original Info

• Title: MindShift: Analyzing Language Models Reactions to Psychological Prompts
• ArXiv ID: 2512.09149
• Date: 2025-12-09
• Authors: Anton Vasiliuk, Irina Abdullaeva, Polina Druzhinina, Anton Razzhigaev, Andrey Kuznetsov

📝 Abstract

Large language models (LLMs) hold the potential to absorb and reflect personality traits and attitudes specified by users. In our study, we investigated this potential using robust psychometric measures. We adapted the most studied test in psychological literature, namely Minnesota Multiphasic Personality Inventory (MMPI) and examined LLMs' behavior to identify traits. To asses the sensitivity of LLMs' prompts and psychological biases we created personalityoriented prompts, crafting a detailed set of personas that vary in trait intensity. This enables us to measure how well LLMs follow these roles. Our study introduces MindShift, a benchmark for evaluating LLMs' psychological adaptability. The results highlight a consistent improvement in LLMs' role perception, attributed to advancements in training datasets and alignment techniques. Additionally, we observe significant differences in responses to psychometric assessments across different model types and families, suggesting variability in their ability to emulate human-like personality traits. Mind-Shift prompts and code for LLM evaluation are open-sourced 1 .

📄 Full Content

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