Abstract

In recent years, large language models (LLMs) have garnered increasing attention from both academia and industry due to their potential to facilitate natural language processing (NLP) and generate highquality text. Despite their benefits, however, the use of LLMs is raising concerns about the reliability of knowledge extraction. The combination of DB research and data science has advanced the state of the art in solving real-world problems, such as merchandise recommendation and hazard prevention [30]. In this discussion, we explore the challenges and opportunities related to LLMs in DB and data science research and education.

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