TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition (2408.09856v1)

Abstract: 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.

• The paper presents TeamLoRA, a new PEFT method that integrates expert collaboration and competition to optimize multi-task learning.
• It features a collaboration module that streamlines matrix operations, thereby boosting training and inference speeds.
• The competition module employs a game-theoretic approach to transfer domain-specific knowledge, enhancing overall model accuracy on benchmark tasks.

The paper "TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition" addresses the limitations of traditional Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA in handling multi-dimensional task scenarios. While LoRA and similar approaches have successfully mitigated GPU memory constraints, their performance in multi-task settings often lacks effectiveness.

To enhance this, the authors propose TeamLoRA, an innovative PEFT method that incorporates both collaboration and competition among task-specific experts. This design seeks to balance efficiency and effectiveness:

1. Collaboration Module: TeamLoRA introduces a novel knowledge-sharing and organizing mechanism aimed at reducing the scale of matrix operations. This approach enhances training and inference speed, addressing one of the core challenges of PEFTs in multi-task environments.
2. Competition Module: By employing a game-theoretic interaction framework, TeamLoRA encourages experts to compete by transferring domain-specific knowledge across varying downstream tasks. This competition among experts is intended to boost overall model performance by leveraging diverse expertise.

The combination of collaboration and competition modules allows TeamLoRA to treat the experts as a cohesive "team," which interacts internally to optimize performance. This method provides a more efficient fine-tuning process that excels at multi-task learning.

To validate TeamLoRA's effectiveness, the paper introduces a comprehensive multi-task evaluation (CME) benchmark, designed to thoroughly assess multi-task learning capabilities. The experiments on CME and other benchmarks highlight TeamLoRA's superior performance in both efficiency and accuracy compared to existing methods.

Overall, TeamLoRA offers a promising framework for enhancing the performance of low-rank adaptation techniques in complex multi-task scenarios, pushing the boundaries of what such models can achieve in terms of speed and accuracy.