提问、回答与检测：基于角色扮演的LLM用于带有问题条件的专家混合模型的人格检测
Ask, Answer, and Detect: Role-Playing LLMs for Personality Detection with Question-Conditioned Mixture-of-Experts
AI总结：
ROME通过角色扮演LLM模拟用户回答心理测量问卷，生成可解释的证据链接语言线索与人格标签，提升人格检测性能。
摘要：
理解人类人格对于网络应用如个性化推荐和心理健康评估至关重要。现有的人格检测研究大多采用“帖子->用户向量->标签”的建模范式，将社交媒体帖子编码为用户表示以预测人格标签（例如MBTI标签）。尽管近年来大语言模型（LLMs）的进步提升了文本编码能力，但这些方法仍受到有限监督信号的限制，因为标签稀缺，且用户语言与抽象心理构念之间的语义映射不明确。我们通过提出ROME，一种新的框架，明确地将心理知识注入到人格检测中。受标准化自我评估测试的启发，ROME利用LLMs的角色扮演能力来模拟用户对经过验证的心理测量问卷的回应。生成的问答级回答将自由形式的用户帖子转化为可解释的、问卷基础的证据，将语言线索与人格标签联系起来，从而提供丰富的中间监督以缓解标签稀缺问题，同时提供一个语义推理链，指导并简化文本到人格映射的学习。一个问题条件的专家混合模块随后联合路由过帖子和问题表示，学习在显式监督下回答问卷项目。预测的答案被总结成一个可解释的答案向量，并与用户表示融合，以最终预测在多任务学习框架中，其中问答服务作为强大的辅助任务用于人格检测。在两个真实世界数据集上的广泛实验表明，ROME在Kaggle数据集上实现了15.41%的改进，优于最先进的基线。
英文摘要：
Understanding human personality is crucial for web applications such as personalized recommendation and mental health assessment. Existing studies on personality detection predominantly adopt a "posts -> user vector -> labels" modeling paradigm, which encodes social media posts into user representations for predicting personality labels (e.g., MBTI labels). While recent advances in large language models (LLMs) have improved text encoding capacities, these approaches remain constrained by limited supervision signals due to label scarcity, and under-specified semantic mappings between user language and abstract psychological constructs. We address these challenges by proposing ROME, a novel framework that explicitly injects psychological knowledge into personality detection. Inspired by standardized self-assessment tests, ROME leverages LLMs' role-play capability to simulate user responses to validated psychometric questionnaires. These generated question-level answers transform free-form user posts into interpretable, questionnaire-grounded evidence linking linguistic cues to personality labels, thereby providing rich intermediate supervision to mitigate label scarcity while offering a semantic reasoning chain that guides and simplifies the text-to-personality mapping learning. A question-conditioned Mixture-of-Experts module then jointly routes over post and question representations, learning to answer questionnaire items under explicit supervision. The predicted answers are summarized into an interpretable answer vector and fused with the user representation for final prediction within a multi-task learning framework, where question answering serves as a powerful auxiliary task for personality detection. Extensive experiments on two real-world datasets demonstrate that ROME consistently outperforms state-of-the-art baselines, achieving improvements (15.41% on Kaggle dataset).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
