Applying Large Language Models (LLM) to Big Data Management: Gaining Deep Insights

The rapid expansion of data in recent years has necessitated the need for innovative strategies for effective data management, and has introduced large language models (LLMs) as a transformative tool in this area. This paper reviews the integration of large language models in big data management and highlights their applications, benefits, and inherent challenges. LLMs excel in data interpretation, automated reporting, search enhancement, and predictive analytics, enabling organizations to extract deep insights from vast data sets. However, challenges such as data quality, privacy concerns, discrimination, and resource intensity need to be addressed to ensure responsible implementation. By employing techniques such as retrieval-augmented generation (RAG) and vector databases, organizations can optimize LLM interactions and improve efficiency and decision-making processes. This literature review emphasizes the need for ongoing research to refine LLM applications in the context of big data and calls for best practices that prioritize ethical considerations and data governance. Ultimately, responsible use of LLMs can foster a culture of data-driven decision-making and help organizations effectively navigate the complexities of the digital space.

کلیدواژه ها:

Large Language Models ، big data ، Deep insight ، Artificial intelligence ، Machine learning