Task Interference

Task interference describes the negative impact one task has on the performance of another when both are processed by a shared system, a significant challenge in multi-task learning (MTL) and large language models (LLMs). Current research focuses on mitigating this interference through architectural innovations, such as employing mixture-of-experts (MoE) models, learnable gating mechanisms to dynamically allocate resources, and explicit task routing with non-learnable primitives to decouple task-specific parameters. Addressing task interference is crucial for improving the efficiency and robustness of AI systems, particularly in complex applications involving multiple tasks or modalities, and for understanding cognitive limitations in human multitasking.