ROME：用角色扮演 LLM 与问题条件 MoE 做人格检测

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Abstract:Understanding human personality is important for personalized recommendation and mental health assessment. Existing personality detection methods typically encode social media posts into user representations and predict personality labels. Despite advances in large language models (LLMs), these methods face limited supervision from scarce labels and an unclear mapping between user language and psychological constructs. We propose ROME, a framework that incorporates psychological knowledge into personality detection. Inspired by standardized self-assessment tests, ROME uses LLM role-playing to simulate users' responses to validated psychometric questionnaires. These question-level answers provide interpretable, questionnaire-grounded evidence and intermediate supervision. A question-conditioned Mixture-of-Experts module jointly routes post and question representations to predict questionnaire answers under explicit supervision. The predicted answers are aggregated into an interpretable answer vector and fused with user representations for final personality prediction in a multi-task learning framework. Experiments on two real-world datasets show that ROME outperforms state-of-the-art baselines, with relative improvements of 13.60% on Kaggle and 19.72% on Pandora. Code is available at this https URL.

Submission history

From: Yifan Lyu [view email]
[v1] Tue, 9 Dec 2025 17:07:54 UTC (531 KB)
[v2] Tue, 20 Oct 2026 07:16:19 UTC (1,067 KB)