Abstract:

Large language models (LLMs), such as GPT-3 and BERT, have revolutionized the field of natural language processing (NLP), offering remarkable capabilities in text generation, translation, summarization, and classification. Among their many applications, LLMs show promise in text classification tasks, where they can automatically categorize text data into predefined categories or labels. This paper presents a comprehensive review of the potential and limitations of utilizing LLMs for text classification through synthetic data generation techniques. We delve into the methodologies employed in generating synthetic data using LLMs, which include techniques such as data augmentation, adversarial training, and transfer learning. These approaches aim to address issues of data scarcity and domain adaptation in text classification tasks. We explore their effectiveness in enhancing text classification performance, demonstrating how synthetic data can improve model generalization and robustness across diverse domains and languages. Additionally, we discuss the challenges and ethical considerations associated with synthetic data generation, including issues related to data privacy, bias amplification, and model fairness. Furthermore, we examine the impact of model size, pretraining data, and fine-tuning strategies on the performance of LLMs in text classification tasks. Recent studies have shown that larger models with access to more diverse pretraining data tend to achieve higher accuracy and better generalization on downstream tasks. Fine-tuning strategies, such as curriculum learning and self-training, can further improve model performance by adapting the model to task-specific data distributions. Through a critical analysis of existing literature and empirical studies, we provide insights into the current state-of-the-art techniques, identify key research gaps, and propose future directions for advancing the utilization of LLMs in text classification through synthetic data generation. This includes exploring novel approaches for generating diverse and representative synthetic data, developing evaluation metrics for assessing the quality of synthetic data, and investigating the long-term societal impacts of deploying LLMs in real-world applications.