PEML: Parameter-efficient Multi-Task Learning with Optimized Continuous Prompts

This paper introduces PEML, a method for parameter-efficient multi-task learning with optimized continuous prompts for Large Language Models. It addresses the shortcomings of existing PEFT methods like LoRA and Prefix Tuning by enabling more efficient fine-tuning across multiple tasks and facilitating resource consolidation.