# Synthetic Text as Data: On Usefulness and Limitations
## 3 Data Explanation
### GPT-Generated Data  
To address the class imbalance present in the MBTI dataset, we generated additional text samples using OpenAI's GPT-3.5 model via the ChatGPT interface. While the primary objective was to supplement underrepresented types, this process also offered a unique opportunity to examine whether GPT-generated text could approximate the value of human-written data in training machine learning models. The generation process involved a carefully designed prompt template for each of the 16 MBTI types. Each prompt instructed GPT to write a scenario-based Instagram post that reflects the personality traits and linguistic style of a given MBTI type. For example, the prompt for ESTJ was the following:  
_\"Could you please write a scenario for an Instagram post that an ESTJ might share, with various situations, totaling 1500 characters? Write it in a way that resembles an Instagram caption, and exclude emojis and hashtags.\"_  
The same structure was applied to all types by substituting the MBTI label. While the prompt design was uniform, maintaining stylistic consistency and personality-specific nuance proved challenging. GPT sometimes produced overly generic or repetitive outputs, and manual inspection was needed to ensure quality and diversity. To rebalance the dataset, we generated 200 samples for each low-resource type (e.g., ESTP, ESFP, ENFJ), aligning the output length with the original dataset by capping responses at 1500 characters.  
The generation process involved several practical challenges. First, a minimal level of manual inspection was required to ensure the validity of the generated text. Second, label leakage was a recurring issue--some responses included explicit type references (e.g., \"As an ENFJ, I often...\"), which were removed during post-processing to avoid biasing the classifier. Third, the outputs showed significant variability in length. Posts under 300 characters were often too sparse to be informative, while those exceeding 1800 characters added unnecessary training cost. We applied length-based filtering to ensure consistency. Details for these points are available in the Appendix A. Additionally, although the prompts explicitly prohibited emojis and hashtags, some appeared and were manually removed during cleanup.  
Figure 1: The distribution of sample counts across the 16 MBTI personality types in the dataset.  
Balancing automation and manual oversight was crucial. While GPT enabled scalable and stylistically fluent generation, ensuring semantic variety and task relevance required iterative refinement. Ultimately, the synthetic samples were combined with the original dataset and used to train MBTI classification models.  
Importantly, these synthetic texts served two distinct purposes in our study. First, they acted as a form of targeted augmentation to improve model performance in imbalanced or low-resource settings. Second, and more critically, they enabled us to investigate a broader question: whether LLM-generated content can function as a valid substitute--not merely a supplement--for human-authored data. This dual role reflects the central focus of our study and frames how the results are interpreted in later sections.