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TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition

Tianwei Lin, Jiang Liu, Wenqiao Zhang, Zhaocheng Li, Yang Dai, Haoyuan Li, Zhelun Yu, Wanggui He, Juncheng Li, Hao Jiang, Siliang Tang, Yueting Zhuang

TL;DR

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.

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.

TeamLoRA: Boosting Low-Rank Adaptation with Expert Collaboration and Competition

TL;DR

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.

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.
Paper Structure (26 sections, 13 equations, 5 figures, 6 tables)

This paper contains 26 sections, 13 equations, 5 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Mixture-of-Experts.
  4. Parameter-Efficient Fine-Tuning.
  5. Multi-LoRA Architectures.
  6. Methods
  7. Problem Formulation
  8. Preliminaries
  9. Low-Rank Adaptation.
  10. Mixture of Experts.
  11. TeamLoRA
  12. Efficient Collaboration among Experts.
  13. Effective Competition among Experts.
  14. Experiments
  15. Benchmark and Setting
  16. ...and 11 more sections

Figures (5)

  • Figure 1: Visualization of training time, inference time and performance for various PEFT methods on the CME benchmark. The radius of the sphere illustrates the relative parameter scale added by different methods.
  • Figure 2: The architecture of TeamLoRA. TeamLoRA employs an asymmetric structure consisting of a general module and multiple expert modules as lightweight auxiliary modules to the pre-trained weights and enhances interactions between experts using a competition mechanism, enhancing the capability for multi-task learning.
  • Figure 3: Stability analysis. (a) illustrates how the number of expert modules impact performance. (b) shows the performance comparison of TeamLoRA under different data scales.
  • Figure 4: Visualization of Efficiency and Loss. (a) describes the relative training and inference latency of TeamLoRA and MoELoRA compared to LoRA. (b) displays the loss convergence.
  • Figure 5: Deep analysis of router. (a) Forward path of expert. (b) Router load visualization.