Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks

Sarfaroz Yunusov; Kaige Chen; Kazi Nishat Anwar; Ali Emami

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arXiv · 2508.21628

Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks

Sarfaroz Yunusov·Kaige Chen·Kazi Nishat Anwar·Ali Emami

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Abstract

As Large Language Models (LLMs) increasingly integrate into everyday workflows, where users shape outcomes through multi-turn collaboration, a critical question emerges: do users with different personality traits systematically prefer certain LLMs over others? We conducted a study with 32 participants evenly distributed across four Keirsey personality types, evaluating their interactions with GPT-4 and Claude 3.5 across four collaborative tasks: data analysis, creative writing, information retrieval, and writing assistance. Results revealed significant personality-driven preferences: Rationals strongly preferred GPT-4, particularly for goal-oriented tasks, while idealists favored Claude 3.5, especially for creative and analytical tasks. Other personality types showed task-dependent preferences. Sentiment analysis of qualitative feedback confirmed these patterns. Notably, aggregate helpfulness ratings were similar across models, showing how personality-based analysis reveals LLM differences that traditional evaluations miss.

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Sarfaroz Yunusov, Kaige Chen, Kazi Nishat Anwar, Ali Emami. 2025-08-29. Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks. https://arxiv.org/abs/2508.21628

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