The recent advancements in the integration of Large Language Models (LLMs) across various domains are significantly reshaping the landscape of data-driven applications. In recommender systems, LLMs are being leveraged to enhance multi-task recommendations by fusing collaborative knowledge, thereby improving the understanding of user interests and intentions. This approach not only refines single-task predictions but also addresses the interrelatedness among different recommendation tasks, leading to more personalized and accurate recommendations. In the healthcare sector, LLMs are revolutionizing clinical trial matching and data standardization by transforming unstructured patient data into enriched, interoperable formats. This advancement promises to streamline cancer care, reduce costs, and improve patient outcomes through more efficient and personalized treatment options. Additionally, LLMs are being employed to tackle the complex challenge of schema matching in data integration, particularly in domains like healthcare and finance, where semantic and structural heterogeneity pose significant barriers. By automating schema matching with self-improving LLM programs, the potential for creating interoperable, ML-ready data is greatly enhanced, which in turn can boost the performance of machine learning models. Overall, the innovative use of LLMs in these areas is paving the way for more sophisticated, data-driven solutions that address long-standing challenges in recommendation systems, healthcare, and data integration.