  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.
