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arXiv · 2412.17023

Parameter-Efficient Interventions for Enhanced Model Merging

Marcin Osial·Daniel Marczak·Bartosz Zieliński
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Abstract

Model merging combines knowledge from task-specific models into a unified multi-task model to avoid joint training on all task data. However, current methods face challenges due to representation bias, which can interfere with tasks performance. As a remedy, we propose IntervMerge, a novel approach to multi-task model merging that effectively mitigates representation bias across the model using taskspecific interventions. To further enhance its efficiency, we introduce mini-interventions, which modify only part of the representation, thereby reducing the additional parameters without compromising performance. Experimental results demonstrate that IntervMerge consistently outperforms the state-of-the-art approaches using fewer parameters.

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Marcin Osial, Daniel Marczak, Bartosz Zieliński. 2024-12-22. Parameter-Efficient Interventions for Enhanced Model Merging. https://arxiv.org/abs/2412.17023

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