Conditional Label Smoothing For LLM-Based Data Augmentation in Medical Text Classification

In this paper, we propose and evaluate a data augmentation technique for improving the classification of text data. Using a large language model (LLM), samples of real-world data and a tailored prompt, we generate additional text samples and use these in combination with real-world data to train a deep neural network (DNN) for the classification of medical sentences. In order to compensate for variations in the augmented data, we apply label smoothing (LS) to render the DNN more robust against mislabeled sentences. Moreover, we introduce a conditional label smoothing (CLS) approach to exploit the classification statistics. CLS re-parameterizes label noise, effectively reducing inter-class confusion. We evaluate our system on privacy-sensitive medical text data where we first show the general benefits of using LS in data augmentation and secondly compare CLS to LS. While LS already leads to an improved F1-score and a better false negative rate, CLS slightly outperforms ordinary LS.