用大模型挖掘论文中隐藏关系，提升新冠研究文献检索质量
Enhancing Document Retrieval in COVID-19 Research: Leveraging Large Language Models for Hidden Relation Extraction
- 利用大语言模型从无标注论文中提取隐含关系
- 相比传统工具，显著增强检索系统可用信息量
- 适合需要高效筛选新冠研究文献的研究者
近年来，新冠疫情暴发导致大量相关文献涌现。由于文献数量庞大，亟需高效的检索系统为研究人员提供有用信息。本文提出Covrelex-SE系统，利用大语言模型（LLMs）从无标注文献中提取当前解析工具无法识别的隐藏关系，从而在检索过程中提供更多有效信息，显著提升检索结果质量。
原文摘要 · Abstract (English)
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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