LLM’s: The Progenitor of Future AI

Large Language Models (LLMs) have transformed the landscape of artificial intelligence (AI) research, making significant contributions to areas such as natural language processing, scientific inquiry, and multimodal applications. This review examines the progression, functionalities, and obstacles related to LLMs. Recent developments in models such as GPT-4 and MiniGPT-4 have showcased substantial enhancements in reasoning capabilities, multimodal integration, and operational efficiency, facilitating applications that range from automated scientific exploration to interactive conversational agents. The review underscores the impact of LLMs across various sectors, including dentistry, where they improve diagnostic precision and treatment strategies, as well as in scientific research, where they aid in generating hypotheses and designing experiments. Furthermore, the emergence of 1-bit LLMs marks a significant shift towards cost-effective and energy-efficient AI solutions, enabling broader implementation. Nevertheless, challenges such as bias, ethical dilemmas, and high computational requirements persist. This paper synthesizes current research to offer a thorough overview of LLM evolution, applications, and prospective research avenues.