Enhancing Document Retrieval in COVID-19 Research: Leveraging Large Language Models for Hidden Relation Extraction
June 23, 2025 Β· Declared Dead Β· π arXiv.org
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Authors
Hoang-An Trieu, Dinh-Truong Do, Chau Nguyen, Vu Tran, Minh Le Nguyen
arXiv ID
2506.18311
Category
cs.IR: Information Retrieval
Cross-listed
cs.CL
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
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