We generated 25000 conversations labeled with Big Five Personality traits using prompt programming at GPT-3. Then we train Big Five classification models with these data and evaluate them with 2500 data from generated dialogues and real conversational datasets labeled in Big Five by human annotators. The results indicated that this approach is promising for creating effective training data. We then compare the performance by different training approaches and models. Our results suggest that using Adapter-Transformers and transfer learning from pre-trained RoBERTa sentiment analysis model will perform best with the generated data. Our best model obtained an accuracy of 0.71 in generated data and 0.65 in real datasets. Finally, we discuss this approach's potential limitations and confidence metric.

翻译：我们通过提示编程在GPT-3中生成了25000条标注有大五人格特质的对话数据。随后使用这些数据训练大五人格分类模型，并利用人工标注大五人格特质的2500条生成对话数据及真实对话数据集对模型进行评测。结果表明，该方法在创建有效训练数据方面具有潜力。我们进一步比较了不同训练方法和模型的性能，发现基于Adapter-Transformers架构并结合预训练RoBERTa情感分析模型的迁移学习在生成数据上表现最优。最佳模型在生成数据上达到0.71的准确率，在真实数据集上达到0.65。最后，我们探讨了该方法存在的潜在局限性与置信度指标。