Advancements in Multi-Domain Learning and LoRA Adaptation

The field of multi-domain learning and LoRA adaptation is witnessing significant advancements, with a focus on improving the efficiency and effectiveness of Large Language Models (LLMs) in handling diverse tasks and domains. Researchers are exploring innovative methods to address the challenges of task interference, domain forgetting, and static bias, leading to the development of more robust and adaptable models. A key direction in this field is the simplification of LoRA architectures, with a emphasis on learning robust shared representations rather than isolating task-specific features. Noteworthy papers in this area include:

  • MoExDA, which proposes a lightweight domain adaptation method to counter static bias in action recognition.
  • ICM-Fusion, which introduces a novel framework that synergizes meta-learning with in-context adaptation to enable multi-task adaptation in pre-trained LoRA models.
  • Align-LoRA, which challenges the prevailing paradigm of using multiple adapters or heads in multi-task learning and instead proposes a simplified single-adapter architecture with an explicit loss to align task representations.

Sources

Separating Shared and Domain-Specific LoRAs for Multi-Domain Learning

MoExDA: Domain Adaptation for Edge-based Action Recognition

Tensorized Clustered LoRA Merging for Multi-Task Interference

ICM-Fusion: In-Context Meta-Optimized LoRA Fusion for Multi-Task Adaptation

Align, Don't Divide: Revisiting the LoRA Architecture in Multi-Task Learning

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