BYOM: Building Your Own Multi-Task Model For Free

• URL: http://arxiv.org/abs/2310.01886v3
• Date: Sat, 3 Feb 2024 15:22:33 GMT
• Title: BYOM: Building Your Own Multi-Task Model For Free
• Authors: Weisen Jiang and Baijiong Lin and Han Shi and Yu Zhang and Zhenguo Li and James T. Kwok
• Abstract summary: BYOM-FFT is for merging fully finetuned models, while BYOM-LoRA is for LoRA-finetuned models. Experiments on computer vision and natural language processing tasks show that the proposed BYOM methods outperform existing merging methods by a large margin.
• Score: 69.63765907216442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, various merging methods have been proposed to build a multi-task model from task-specific finetuned models without retraining. However, existing methods suffer from a large performance deterioration compared to using multiple task-specific models. In this paper, we propose to inject task-specific knowledge into the merged model and design two parameter-efficient approaches (BYOM-FFT and BYOM-LoRA) to Build Your Own Multi-task model. BYOM-FFT is for merging fully finetuned models, while BYOM-LoRA is for LoRA-finetuned models. Both methods are data-free and computation-efficient. Extensive experiments on computer vision and natural language processing tasks show that the proposed BYOM methods outperform existing merging methods by a large margin. Moreover, BYOM-FFT is general and can be integrated into existing merging methods to further boost performance.

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