{"ID":2876769,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.21628","arxiv_id":"2508.21628","title":"Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks","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.","short_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 distribute...","url_abs":"https://arxiv.org/abs/2508.21628","url_pdf":"https://arxiv.org/pdf/2508.21628v1","authors":"[\"Sarfaroz Yunusov\",\"Kaige Chen\",\"Kazi Nishat Anwar\",\"Ali Emami\"]","published":"2025-08-29T13:42:26Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.HC\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}