Synthetic Text as Data: On Usefulness and Limitations
Seoyeon Choi, Jaein Sim, Guebin Choi · Applied Sciences · 2025
This study investigates the utility of GPT-generated text as a training resource in supervised learning, focusing on two perspectives: its effectiveness as an augmentation tool in data-scarce or class-imbalanced settings and its potential as a substitute for human-written data. Using MBTI personality classification as a benchmark task, we conducted controlled experiments under both class imbalance and few-shot learning conditions. Results showed that GPT-generated text could improve classification performance when used to supplement underrepresented classes. However, when synthetic data fully replace real data, performance declines significantly—particularly in tasks requiring fine-grained semantic distinctions. Further analysis reveals that GPT outputs often capture only partial personality traits, enabling coarse-level classification but falling short in nuanced cases. These findings suggest that GPT-generated text can function as a conditional training resource, with its effectiveness closely tied to the granularity of the classification task.