Dynamic Parameter Morphogenesis for Adaptable Task Specialization in Large Language Models to Self-Directed Learning

Abstract The rapid evolution of artificial intelligence has led to the development of models capable of performing a wide array of tasks; however, the adaptability of such models to novel and diverse applications remains a significant challenge. Introducing Dynamic Parameter Morphogenesis, a novel methodology that enables Large Language Models (LLMs) to autonomously reconfigure their internal parameters in response to varying task demands, thereby enhancing adaptability and performance across multiple domains. This approach reduces reliance on extensive labeled datasets and manual fine-tuning, facilitating more efficient utilization of computational resources. Empirical evaluations demonstrate that LLMs employing Dynamic Parameter Morphogenesis achieve superior performance metrics and exhibit rapid adaptation to new tasks, showing the potential of this methodology to transform the deployment and scalability of LLMs in various applications.