Enhancing Document Retrieval in COVID-19 Research: Leveraging Large Language Models for Hidden Relation Extraction
This addresses retrieval challenges for researchers during sudden pandemics like COVID-19, but appears incremental as it builds on existing systems.
The authors tackled the problem of inefficient document retrieval in COVID-19 research by using large language models to extract hidden relationships from unlabeled publications, resulting in more high-quality search results for the Covrelex-SE system.
In recent years, with the appearance of the COVID-19 pandemic, numerous publications relevant to this disease have been issued. Because of the massive volume of publications, an efficient retrieval system is necessary to provide researchers with useful information if an unexpected pandemic happens so suddenly, like COVID-19. In this work, we present a method to help the retrieval system, the Covrelex-SE system, to provide more high-quality search results. We exploited the power of the large language models (LLMs) to extract the hidden relationships inside the unlabeled publication that cannot be found by the current parsing tools that the system is using. Since then, help the system to have more useful information during retrieval progress.