Tint Your Models Task-wise for Improved Multi-task Model Merging

• URL: http://arxiv.org/abs/2412.19098v1
• Date: Thu, 26 Dec 2024 07:42:06 GMT
• Title: Tint Your Models Task-wise for Improved Multi-task Model Merging
• Authors: Aecheon Jung, Seunghwan Lee, Dongyoon Han, Sungeun Hong,
• Abstract summary: We propose Model Tinting, a test-time approach that introduces a single task-specific layer for each task as trainable adjustments. Our method jointly trains merging coefficients and task-specific layers, which effectively reduces task conflicts with minimal additional costs. Our method achieves state-of-the-art performance across both computer vision and natural language processing tasks.
• Score: 17.496018757317824
• License:
• Abstract: Traditional model merging methods for multi-task learning (MTL) address task conflicts with straightforward strategies such as weight averaging, sign consensus, or minimal test-time adjustments. This presumably counts on the assumption that a merged encoder still retains abundant task knowledge from individual encoders, implying that its shared representation is sufficiently general across tasks. However, our insight is that adding just a single trainable task-specific layer further can bring striking performance gains, as demonstrated by our pilot study. Motivated by this finding, we propose Model Tinting, a new test-time approach that introduces a single task-specific layer for each task as trainable adjustments. Our method jointly trains merging coefficients and task-specific layers, which effectively reduces task conflicts with minimal additional costs. Additionally, we propose a sampling method that utilizes the difference in confidence levels of both merged and individual encoders. Extensive experiments demonstrate our method's effectiveness, which achieves state-of-the-art performance across both computer vision and natural language processing tasks and significantly surpasses prior works. Our code is available at https://github.com/AIM-SKKU/ModelTinting.

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