TeamLoRA：通过专家协作与竞争增强低秩适配
TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition
AI总结：
针对LoRA等PEFT方法在多任务场景下性能不足的问题，提出TeamLoRA方法，通过专家协作与竞争机制平衡效率与效果，在自建CME等基准上验证了其有效性和高效性。
摘要：
尽管低秩适配（LoRA）等参数高效微调（PEFT）方法已有效解决了微调过程中的GPU内存限制问题，但它们的性能往往不尽如人意，尤其是在多维度任务场景中。为解决这一问题，一个直接的方案是引入任务特定的LoRA模块作为领域专家，通过对多个专家能力的建模来增强多任务学习的通用能力。尽管这一思路前景可观，但这些额外组件往往会增加训练和推理过程的复杂度，违背了PEFT设计的高效性初衷。\n考虑到这一点，我们提出了一种创新的PEFT方法——TeamLoRA，它包含一个专家协作与竞争模块，从而在有效性和效率之间实现了恰当的平衡：（i）在协作方面，我们设计了一种新颖的知识共享与组织机制，可适当降低矩阵运算的规模，从而提升训练和推理速度。（ii）在竞争方面，我们提出利用博弈论交互机制作用于专家，鼓励专家在面对不同下游任务时迁移其领域特定知识，从而提升性能。\n通过这种方式，TeamLoRA巧妙地将专家们连接成一个存在内部协作与竞争的“团队”，为多任务学习提供了一种更快、更准确的PEFT范式。为验证TeamLoRA的优越性，我们构建了一个综合多任务评估（CME）基准，以全面评估多任务学习的能力。在我们的CME基准及其他基准上开展的实验表明了TeamLoRA的有效性和高效性。我们的项目可在https://github.com/Lin-Tianwei/TeamLoRA获取。
英文摘要：
While Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA have effectively addressed GPU memory constraints during fine-tuning, their performance often falls short, especially in multidimensional task scenarios. To address this issue, one straightforward solution is to introduce task-specific LoRA modules as domain experts, leveraging the modeling of multiple experts' capabilities and thus enhancing the general capability of multi-task learning. Despite promising, these additional components often add complexity to the training and inference process, contravening the efficient characterization of PEFT designed for. Considering this, we introduce an innovative PEFT method, TeamLoRA, consisting of a collaboration and competition module for experts, and thus achieving the right balance of effectiveness and efficiency: (i) For collaboration, a novel knowledge-sharing and -organizing mechanism is devised to appropriately reduce the scale of matrix operations, thereby boosting the training and inference speed. (ii) For competition, we propose leveraging a game-theoretic interaction mechanism for experts, encouraging experts to transfer their domain-specific knowledge while facing diverse downstream tasks, and thus enhancing the performance. By doing so, TeamLoRA elegantly connects the experts as a "Team" with internal collaboration and competition, enabling a faster and more accurate PEFT paradigm for multi-task learning. To validate the superiority of TeamLoRA, we curate a comprehensive multi-task evaluation(CME) benchmark to thoroughly assess the capability of multi-task learning. Experiments conducted on our CME and other benchmarks indicate the effectiveness and efficiency of TeamLoRA. Our project is available at https://github.com/Lin-Tianwei/TeamLoRA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
