Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification

Synthetic data augmentation via large language models (LLMs) allows researchers to leverage additional training data, thus enhancing the performance of downstream tasks, especially when real-world data is scarce. However, the generated data can deviate from the real-world data, and this misalignment can bring deficient outcomes while applying the trained model to applications. Therefore, we proposed efficient weighted-loss approaches to align synthetic data with realworld distribution by emphasizing high-quality and diversified data generated by LLMs with using merely a little real-world data. We empirically assessed the effectiveness of our method on multiple text classification tasks, and the results showed leveraging our approaches on a BERT-level model robustly outperformed standard cross-entropy and other data weighting approaches, providing potential solutions to effectively leveraging synthetic data from any suitable data generator for model training. The quantity and quality of data play a significant role in many tasks of Natural Language Processing (NLP). However, due to the scarcity of data in a particular domain for a specific task, we may need expertise to collect such data, resulting in budget limitations. Fortunately, Large language models (LLMs) provide a practical solution to this problem. LLMs, such as GPT series (Brown et al., 2020; OpenAI, 2022; OpenAI et al., 2024), can be leveraged to generate synthetic data that mimics real-world examples, thereby enriching the training set (Wang et al., 2023). However, training models with LLM-generated data can lead to drawbacks such as model collapse (Shumailov et al., 2023; Dohmatob et al., 2024), tail phenomena, reinforcing LM biases (Wang et al., 2023). Moreover, based on our empirical study, the performance of models trained on synthetic data without proper processing can be lower than models trained on much smaller real-world data (Sec. Previous works took data filtering strategy to get high quality or variant data (Dubey et al., 2024; MetaAI, 2024; Chiang et al., 2023; West et al., 2022).