  With the advent of large language models (LLMs), there has been growing
interest in exploring their potential for medical applications. This research
aims to investigate the ability of LLMs, specifically ChatGPT, in the context
of pharmacovigilance event extraction, of which the main goal is to identify
and extract adverse events or potential therapeutic events from textual medical
sources. We conduct extensive experiments to assess the performance of ChatGPT
in the pharmacovigilance event extraction task, employing various prompts and
demonstration selection strategies. The findings demonstrate that while ChatGPT
demonstrates reasonable performance with appropriate demonstration selection
strategies, it still falls short compared to fully fine-tuned small models.
Additionally, we explore the potential of leveraging ChatGPT for data
augmentation. However, our investigation reveals that the inclusion of
synthesized data into fine-tuning may lead to a decrease in performance,
possibly attributed to noise in the ChatGPT-generated labels. To mitigate this,
we explore different filtering strategies and find that, with the proper
approach, more stable performance can be achieved, although constant
improvement remains elusive.
