Rebuttal by Authors
Thank you so much for your review and feedback. We further address your comments regarding Weakness 2 and Weakness 3 as follows:
Answer to Weakness 2:
1) **Model finetuning and model merging are fundamentally different tasks with different targets**. For model fine-tuning, it consists of full-parameter fine-tuning that pursues fine-tuning performance and parameter-efficient fine-tuning (PEFT) that pursues fine-tuning efficiency, both of which have the same target of customizing models for specific domains, **necessitating labeled datasets and sufficient computational resources for supervised training**. For model merging, under the background that there exists an exponentialy increasing number of pre-trained or finetuned model weights, it targets to take advantage of these existing weights and obtain a single model with multi-task abilities, **without the need for labeled data or supervised training**.
2) **Model merging can be combined with kinds of model fine-tuning methods**. More specifically, the proposed **EMR-Merging can be applied to not only fully finetuned models but also PEFT models**, as shown in Tab. 6 of our paper. We only need the fine-tuned adapters on each task, and EMR-Merging can merge these adapters into a single adapter with a few masks and rescalers, whose overhead is less than storing all the adapters while achieving performance close to that of multiple individual adapters.
3) **Model finetuning and model merging are both significant techniques**. In this paper, our EMR-Merging focuses on realizing tuning-free and high-performance model merging instead of reducing storage space requirements as much as possible like PEFT. Besides, **model merging and PEFT can be easily combined to achieve further parameter reduction**, which is a potential area for future work. For example, there have been some studies focusing on merging PEFT models [1,2].
Answer to Weakness 3:
1) When **compared to multiple individual 8-bit models**, EMR-Merging that consists of one 8-bit model and several masks and scalars can still significantly reduce the parameter numbers.
2) When **compared to other merging methods**, EMR-Merging shows significantly better performance requring no tuning, demonstrating its greater applicability.
3) **A potential solution to your concern is using PEFT techniques to train the quantilized models and merge the PEFT modules using EMR-Merging**. This can minimize the number of parameters on multiple tasks, which will be included in our future work.
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**References**
[1] Parameter efficient multi-task model fusion with partial linearization, ICLR 2024.
[2] Composing Parameter-Efficient Modules with Arithmetic Operations, NeurIPS 2023.