Multitask Model

Multitask models aim to combine the capabilities of multiple single-task models into a single, unified system, improving efficiency and performance compared to training separate models for each task. Current research focuses on efficient model merging techniques, addressing challenges like parameter interference and achieving optimal trade-offs between different tasks, often employing methods such as weighted parameter averaging, quadratic approximation, and iterative optimization algorithms. These advancements are significant because they enable the creation of more versatile and powerful AI systems, reducing computational costs and improving performance across diverse applications, from natural language processing to energy consumption monitoring.