SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning
Summary: arXiv:2604.19048v1 Announce Type: cross
Abstract
The combination of Mixture-of-Experts (MoE) and Low-Rank Adaptation (LoRA) has shown significant potential for enhancing the multi-task learning capabilities of Large Language Models. However, existing methods face two primary challenges:
- Imprecise Routing: The current MoE-LoRA method fails to explicitly match input semantics with expert capabilities, leading to weak expert specialization.
- Uniform Weight Fusion: Strategies in this area struggle to provide adaptive update strengths, which overlook the varying complexity of different tasks.
Introducing SAMoRA
To address these limitations, we propose SAMoRA (Semantic-Aware Mixture of LoRA Experts), a novel parameter-efficient fine-tuning framework tailored for task-adaptive learning. SAMoRA introduces two key mechanisms aimed at improving performance and adaptability:
- Semantic-Aware Router: This innovative router explicitly aligns textual semantics with the most suitable experts, ensuring precise routing. By leveraging semantic information, SAMoRA enhances the effectiveness of expert selection, allowing for more specialized handling of diverse tasks.
- Task-Adaptive Scaling: A mechanism designed to dynamically regulate expert contributions based on specific task requirements. This scaling allows SAMoRA to adaptively adjust the influence of each expert depending on the complexity and demands of the task at hand.
Regularization Objective
In addition to the aforementioned mechanisms, SAMoRA proposes a novel regularization objective that jointly promotes expert specialization and effective scaling. This dual focus is crucial for optimizing the performance of multi-task learning systems, ensuring that each expert can develop its unique strengths while contributing appropriately to the overall task performance.
Experimental Results
Extensive experiments conducted on multiple multi-task benchmarks demonstrate that SAMoRA significantly outperforms state-of-the-art methods. The results indicate not only an enhancement in task-specific performance but also excellent task generalization capabilities. This means that SAMoRA is not only effective for specific tasks but can also generalize well across various applications.
Conclusion
SAMoRA represents a significant advancement in the realm of task-adaptive learning, combining the strengths of Mixture-of-Experts and Low-Rank Adaptation. Through innovative mechanisms for semantic alignment and adaptive scaling, SAMoRA effectively addresses the challenges posed by existing methodologies. Researchers and practitioners in the field of AI and machine learning can access the source code for SAMoRA at https://github.com/boyan-code/SAMoRA.