ISI Journals

In the era of digital transformation, the explosion of data has necessitated more sophisticated techniques to extract meaningful insights. Attention mechanisms have emerged as a pivotal advancement in artificial intelligence (AI), revolutionizing both big data analysis and large language models (LLMs). By allowing models to focus selectively on the most relevant portions of input data, attention mechanisms have significantly enhanced the accuracy, efficiency, and scalability of generative AI applications. This paper explores the transformative impact of attention mechanisms in big data analysis and LLMs, highlighting their role in improving natural language understanding, supporting AI in business decision-making, and advancing prompt engineering. Through an in-depth examination of relevant research and practical methodologies, we assess how attention-based architectures, such as transformers, are reshaping the AI landscape and driving innovation across industries.