Publication

Abstract : Large Language Models, including GPT, LLaMA, and PaLM, have gained attention due to their unparalleled performance in natural language tasks, recommendation systems, and other areas. These models incorporate massive datasets and billions of parameters for sophisticated language understanding and generation. Challenges encompass model interpretability and resource requirements. Research efforts have primarily concentrated on methods such as fine-tuning, prompt tuning, and explainability frameworks to enhance the robustness and controllability of LLMs. Significant advancements, driven by the scaling law and transformer architectures, have contributed to notable progress; however, concerns surrounding ethics, computational efficiency, and the necessity for transparent explanations persist. LLMs are utilized in a broad range of fields—including medicine, education, and finance—yet careful consideration of these challenges is essential in the application of real-world LLMs. Furthermore, LLMs are progressively implemented in sectors like medicine, education, and finance, but their deployment in practical applications heavily relies on the assessment of ethical considerations, biases, and substantial computational expenses.