基于混合策略训练的连续输出人格检测模型
Continuous Output Personality Detection Models via Mixed Strategy Training
AI总结：
针对传统人格模型仅输出二元结果的问题，本文提出混合策略训练的连续输出人格检测模型，基于PANDORA数据集微调RoBERTa-base并结合MLP等策略，性能显著优于传统方法，可提升多领域应用价值。
摘要：
传统人格模型仅能输出二元结果。本文提出一种采用混合策略训练人格检测模型的新方法，可生成连续输出值。我们利用包含Reddit评论大量人格标注的PANDORA数据集，开发出能高精度预测大五人格特质的模型。该方法对RoBERTa-base模型进行微调，结合多层感知机（MLP）集成、超参数调优等多种策略。结果表明，我们的模型显著优于传统二元分类方法，可提供精确的人格特质连续输出，从而提升其在AI、心理学、人力资源、营销及医疗保健领域的应用价值。
英文摘要：
The traditional personality models only yield binary results. This paper presents a novel approach for training personality detection models that produce continuous output values, using mixed strategies. By leveraging the PANDORA dataset, which includes extensive personality labeling of Reddit comments, we developed models that predict the Big Five personality traits with high accuracy. Our approach involves fine-tuning a RoBERTa-base model with various strategies such as Multi-Layer Perceptron (MLP) integration, and hyperparameter tuning. The results demonstrate that our models significantly outperform traditional binary classification methods, offering precise continuous outputs for personality traits, thus enhancing applications in AI, psychology, human resources, marketing and health care fields.
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。
