The advent of large language models (LLMs) has revolutionized various domains of natural language processing, including bibliographic data retrieval. This paper explores the potential of LLMs to enhance the accuracy and efficiency of retrieving bibliographic data from vast digital repositories. By leveraging the deep learning capabilities of LLMs, we propose a novel approach that surpasses traditional keyword-based search methods. Our methodology involves fine-tuning pre-trained LLMs on a comprehensive dataset of bibliographic records, enabling the model to understand and interpret complex queries more effectively. Experimental results demonstrate that our approach significantly improves precision and recall metrics, thereby reducing the retrieval of irrelevant data and enhancing the overall user experience. Furthermore, we discuss the implications of these findings for academic research, library sciences, and digital archiving, highlighting the transformative potential of LLMs in organizing and accessing scholarly information. This study provides a foundation for future research into the integration of LLMs with bibliographic databases, aiming to develop smarter, more intuitive information retrieval systems.

Jingjing Qiao. Improving Bibliographic Data Retrieval through Large Language Models. The Frontiers of Society, Science and Technology (2024), Vol. 6, Issue 6: 49-55. https://doi.org/10.25236/FSST.2024.060608.

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