# Synthetic Text as Data: On Usefulness and Limitations
## 2 Related Works  
This study centers on two main questions: First, can text generated by large language models (LLMs) such as GPT be used effectively for data augmentation? Second, can such synthetic text function as a valid substitute or supplement for real-world training data? Accordingly, we review related works along these two lines of inquiry.  
We begin with prior research on data augmentation. In natural language processing (NLP), various augmentation techniques have long been used to address class imbalance and low-resource conditions. Traditional methods such as synonym replacement, back-translation, and random insertion or deletion offer computational simplicity and ease of implementation [1,2]. However, these methods often fall short in preserving semantic coherence or contextual appropriateness [3]. With the recent advancements in LLMs, especially the GPT series, a new line of work has emerged focusing on generating more fluent and context-aware synthetic text for data augmentation. For instance, [PERSON] et al. [4] rephrased training samples using ChatGPT to improve classification accuracy. [PERSON] et al. [5] leveraged prompt-based paraphrasing with contrastive learning for effective few-shot classification. [PERSON] et al. [6] applied GPT-generated text to improve performance in neural machine translation, and [PERSON] et al. [7] demonstrated that ChatGPT-based paraphrases show comparable diversity and robustness to human-written examples in intent classification tasks. While these approaches have mainly been applied to binary or sentence-level tasks, there is growing interest in expanding their application to more fine-grained and complex classification problems, where their potential remains to be further explored.  
We now turn to the question of synthetic text validity. While GPT-generated text is often fluent and grammatically correct--sometimes indistinguishable from human-authored content on the surface--its usefulness as training data is still under investigation. However, the question of whether GPT-generated text is truly useful as training data remains actively debated. Prior studies have primarily focused on leveraging the generative abilities of LLMs to perform specific tasks [8,9], while relatively few have quantitatively evaluated the effectiveness of synthetic text when used directly as training input. In response, a number of recent works have begun to examine the informativeness and generalizability of LLM-generated content. For instance, [PERSON] et al. [10] found that models trained on data generated by other LLMs tend to underperform compared to those trained on real data. [PERSON] et al. [11] observed that GPT-generated samples often repeat known patterns and lack novelty, while [PERSON] et al. [12] proposed the Scarecrow framework to systematically analyze errors in GPT-3 outputs and highlight subtle distinctions from human-authored text. [PERSON] et al. [13] systematically explored this issue by evaluating the performance of classifiers trained on GPT-generated text across various tasks. Their results show that while synthetic data can be valuable in low-subjectivity settings, its effectiveness significantly declines in more subjective or fine-grained tasks.  
Building on these discussions, our study aims to evaluate the value of GPT-generated text as a training resource, either as a supplement to or substitute for real data. In particular, we investigate how such synthetic samples perform under data-scarce and imbalanced conditions, offering a quantitative analysis of their practical utility across different experimental settings.