Intuition-aware Mixture-of-Rank-1-Experts for Parameter Efficient Finetuning

arXiv:2404.08985v1 Announce Type: new
Abstract: Large Language Models (LLMs) have demonstrated significant potential in performing multiple tasks in multimedia applications, ranging from content generation to interactive entertainment, and artistic creation. However, the diversity of downstream tasks in multitask scenarios presents substantial adaptation challenges for LLMs. While traditional methods often succumb to knowledge confusion on their monolithic dense models, Mixture-of-Experts (MoE) has been emerged as a promising solution with its sparse architecture for effective task decoupling. Inspired by the principles of …

abstract applications arxiv challenges content generation cs.ai cs.lg diversity entertainment experts finetuning however interactive intuition knowledge language language models large language large language models llms multimedia multiple tasks type