Multitask Finetuning

Multitask finetuning adapts pre-trained large language models (LLMs) or foundation models to multiple downstream tasks simultaneously, aiming to improve efficiency and performance compared to training separate models for each task. Current research explores various architectures, including Mixture-of-Experts (MoE) and Low-Rank Adaptation (LoRA) methods, to enhance parameter efficiency and mitigate issues like catastrophic forgetting and negative transfer. This approach is significant because it offers improved generalization to unseen tasks, faster training, and more efficient deployment of powerful models across diverse applications, impacting fields like natural language processing, computer vision, and code generation.