CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging

• URL: http://arxiv.org/abs/2503.01874v1
• Date: Wed, 26 Feb 2025 12:38:55 GMT
• Title: CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging
• Authors: Zongzhen Yang, Binhang Qi, Hailong Sun, Wenrui Long, Ruobing Zhao, Xiang Gao,
• Abstract summary: Model merging based on task vectors provides an efficient way to integrate multiple task-specific models into a multitask model without retraining.<n>Recent works have endeavored to address the conflicts between task vectors, one of the significant challenges faced by model merging, through sparsification.<n>We propose CABS (Conflict-Aware and Balanced Sparsification), consisting of Conflict-Aware Sparsification (CA) and Balanced Sparsification (BS)
• Score: 11.2457468104475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model merging based on task vectors, i.e., the parameter differences between fine-tuned models and a shared base model, provides an efficient way to integrate multiple task-specific models into a multitask model without retraining. Recent works have endeavored to address the conflicts between task vectors, one of the significant challenges faced by model merging, through sparsification; however, two issues significantly limit their performance: high parameter overlap and unbalanced weight distribution. To address these issues, we propose a simple, yet effective framework called CABS (Conflict-Aware and Balanced Sparsification), consisting of Conflict-Aware Sparsification (CA) and Balanced Sparsification (BS). CA can reduce parameter overlap by applying masks during sequential pruning, ensuring that each task vector retains distinct, non-overlapping parameters. BS leverages $n$: $m$ pruning to preserve critical weights while maintaining an even distribution across layers. Our comprehensive experiments demonstrate that CABS outperforms state-of-the-art methods across diverse tasks and model sizes.

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