Few-Shot Learning for Identification of COVID-19 Symptoms Using Generative Pre-trained Transformer Language Models

ABSTRACT

Since the onset of the COVID-19 pandemic, social media users have shared their personal experiences related to the viral infection. Their posts contain rich information of symptoms that may provide useful hints to advancing the knowledge body of medical research and supplement the discoveries from clinical settings. Identification of symptom expressions in social media text is challenging, partially due to lack of annotated data. In this study, we investigate utilizing few-shot learning with generative pre-trained transformer language models to identify COVID-19 symptoms in Twitter posts. The results of our approach show that large language models are promising in more accurately identifying symptom expressions in Twitter posts with small amount of annotation effort, and our method can be applied to other medical and health applications where abundant of unlabeled data is available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.