CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging
This addresses performance degradation in model merging for multi-task AI systems, offering an incremental improvement over existing techniques.
The paper tackled the problem of knowledge conflicts in multi-task model merging, which degrade performance, by proposing CAT Merging, a training-free framework that trims conflict-prone components, achieving average accuracy improvements of up to 2.5% and 2.0% over state-of-the-art methods.
Multi-task model merging offers a promising paradigm for integrating multiple expert models into a unified model without additional training. Existing state-of-the-art techniques, such as Task Arithmetic and its variants, merge models by accumulating task vectors -- the parameter differences between pretrained and finetuned models. However, task vector accumulation is often hindered by knowledge conflicts, leading to performance degradation. To address this challenge, we propose Conflict-Aware Task Merging (CAT Merging), a novel training-free framework that selectively trims conflict-prone components from the task vectors. CAT Merging introduces several parameter-specific strategies, including projection for linear weights and masking for scaling and shifting parameters in normalization layers. Extensive experiments on vision, language, and vision-language tasks demonstrate that CAT Merging effectively suppresses knowledge conflicts, achieving average accuracy improvements of up to 2.5% (ViT-B/32) and 2.0% (ViT-L/14) over state-of-the-art methods.