Exploring the Synergy of Generative AI and Large Language Models Advancing Machine Learning Applications in Data-Driven Research
Keywords:
Generative AI, Large Language Models, Machine Learning, Data-Driven Research, Hybrid Framework.Abstract
Generative AI and the large language models (LLMs) are powerful new components in ML, and platforms capable of supporting these technologies deliver remarkably sophisticated data-driven applications. This paper explores the joint application of such technologies along with its potential of enhances other machine learning implementations. A detailed exploration of how generative AI models like GANs and diffusion models, converge with LLMs to solve both natural language processing and multimodal data synthesis problems are revealed through this paper. Our empirical evidence illustrates how the co-deployment of generative AI models and LLMs is shown to improve performance by augmenting data scenarios as well as applying an integrated approach to context retrieval and prediction model accuracy. Our technical approach provides a new framework that integrates generative modeling with LLMs and aims to accelerate research pipelines mainly involving biomedical data analysis and knowledge discovery tasks. Our study shows that this combination will be fundamental reconfiguration of new paradigm of machine learning to provide more robust and advanced scale systems with intelligence. In short, we need generative AI with LLMs to create our strong foundation to build data-driven innovations on top of as we enter different sectors.
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