  Adapting models pre-trained on large-scale datasets to a variety of
downstream tasks is a common strategy in deep learning. Consequently,
parameter-efficient fine-tuning methods have emerged as a promising way to
adapt pre-trained models to different tasks while training only a minimal
number of parameters. While most of these methods are designed for single-task
adaptation, parameter-efficient training in Multi-Task Learning (MTL)
architectures is still unexplored. In this paper, we introduce MTLoRA, a novel
framework for parameter-efficient training of MTL models. MTLoRA employs
Task-Agnostic and Task-Specific Low-Rank Adaptation modules, which effectively
disentangle the parameter space in MTL fine-tuning, thereby enabling the model
to adeptly handle both task specialization and interaction within MTL contexts.
We applied MTLoRA to hierarchical-transformer-based MTL architectures, adapting
them to multiple downstream dense prediction tasks. Our extensive experiments
on the PASCAL dataset show that MTLoRA achieves higher accuracy on downstream
tasks compared to fully fine-tuning the MTL model while reducing the number of
trainable parameters by 3.6x. Furthermore, MTLoRA establishes a Pareto-optimal
trade-off between the number of trainable parameters and the accuracy of the
downstream tasks, outperforming current state-of-the-art parameter-efficient
training methods in both accuracy and efficiency. Our code is publicly
available.
