Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911130142638080 |
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| author | Yunusov, Sarfaroz Chen, Kaige Anwar, Kazi Nishat Emami, Ali |
| author_facet | Yunusov, Sarfaroz Chen, Kaige Anwar, Kazi Nishat Emami, Ali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21628 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks Yunusov, Sarfaroz Chen, Kaige Anwar, Kazi Nishat Emami, Ali Computation and Language Human-Computer Interaction 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. |
| title | Personality Matters: User Traits Predict LLM Preferences in Multi-Turn Collaborative Tasks |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2508.21628 |