Parameter Competition Balancing for Model Merging (2410.02396v1)

Abstract: While fine-tuning pretrained models has become common practice, these models often underperform outside their specific domains. Recently developed model merging techniques enable the direct integration of multiple models, each fine-tuned for distinct tasks, into a single model. This strategy promotes multitasking capabilities without requiring retraining on the original datasets. However, existing methods fall short in addressing potential conflicts and complex correlations between tasks, especially in parameter-level adjustments, posing a challenge in effectively balancing parameter competition across various tasks. This paper introduces an innovative technique named PCB-Merging (Parameter Competition Balancing), a lightweight and training-free technique that adjusts the coefficients of each parameter for effective model merging. PCB-Merging employs intra-balancing to gauge parameter significance within individual tasks and inter-balancing to assess parameter similarities across different tasks. Parameters with low importance scores are dropped, and the remaining ones are rescaled to form the final merged model. We assessed our approach in diverse merging scenarios, including cross-task, cross-domain, and cross-training configurations, as well as out-of-domain generalization. The experimental results reveal that our approach achieves substantial performance enhancements across multiple modalities, domains, model sizes, number of tasks, fine-tuning forms, and LLMs, outperforming existing model merging methods. The code is publicly available at: \url{https://github.com/duguodong7/pcb-merging}.

• The paper introduces Pcb-Merging, a method that balances intra-task importance and inter-task similarities to enhance model merging.
• It utilizes a PCB matrix to rescale parameters and drop redundancies, achieving up to 4.3% improvement in NLP tasks.
• Experimental results on T5, ViT, and LLaMa2 models demonstrate its data-free design and robust performance across domains.

Parameter Competition Balancing for Model Merging

The paper presents a novel approach named Pcb-Merging (Parameter Competition Balancing) aimed at enhancing model merging techniques by addressing parameter competition across tasks. Traditional methods, such as simple weight averaging, have struggled to manage conflicts and intricate correlations between tasks, especially at the parameter level. This paper introduces a sophisticated method to adjust parameters during model merging, significantly improving performance without additional training.

Methodology Overview

Pcb-Merging employs two key balancing mechanisms: intra-balancing and inter-balancing. Intra-balancing focuses on assessing the importance of parameters within individual tasks, using a nonlinear activation function to emphasize key parameters and mitigate redundant ones. The method incorporates the number of tasks to adjust the suppression of redundant parameters. Inter-balancing evaluates parameter similarities across different tasks, enabling a careful adjustment of task vector scaling factors to manage task competition effectively.

The proposed approach involves constructing a PCB matrix to guide the rescaling of parameters and dropping parameters with low importance scores. This is followed by combining the modulated task vectors into a pretrained model to produce a merged model. The method's lightweight, data-free nature makes it highly efficient for practical deployment.

Experimental Evaluation

The paper evaluates Pcb-Merging across various scenarios including cross-task, cross-domain, cross-training configurations, and out-of-domain generalization. Using models such as T5, ViT, and LLaMa2, the approach demonstrates superior performance over existing methods, highlighting its adaptability and robustness. Here are some highlighted results:

• Cross-Task Merging: Achieved an average improvement of 4.3% over seven NLP tasks with T5-base models, outperforming previous state-of-the-art techniques.
• PEFT Model Merging: With (IA)$^3$ models, Pcb-Merging displayed an enhancement of 1.3% across eleven tasks.
• LLM Merging: Improved overall performance by 0.6% on tasks involving Chinese language proficiency, mathematical reasoning, and code generation.
• Vision Model Merging: Showed significant gains of 3.5% in ViT-B/32 models over the strongest baselines.

For out-of-domain generalization, the merged models exhibited superior capability in handling unseen datasets, suggesting promising applications in situations with domain shifts.

Implications and Future Prospects

The paper's findings hold substantial implications for enhancing model efficiency and flexibility without retraining, addressing data privacy concerns, and improving generalization across tasks and domains. The introduction of Pcb-Merging as a self-aware and cross-aware mechanism sets a new direction for future research in parameter-efficient model merging techniques. Future developments could explore extending this methodology to handle varied model architectures and enhancing understanding of parameter interactions and their impact on task performance.

In conclusion, Pcb-Merging addresses a critical gap in current model merging techniques, offering a training-free and adaptable solution. Its robust experimental validation across multiple scenarios underscores its potential to drive significant advancements in AI model efficiency and adaptability.