Related papers: Boosting Large Language Models with Mask Fine-Tuning

Boosting Large Language Models with Mask Fine-Tuning

URL: http://arxiv.org/abs/2503.22764v1
Date: Thu, 27 Mar 2025 20:17:57 GMT
Title: Boosting Large Language Models with Mask Fine-Tuning
Authors: Mingyuan Zhang, Yue Bai, Huan Wang, Yizhou Wang, Qihua Dong, Yun Fu,
Abstract summary: We introduce Mask Fine-Tuning (MFT) to show that properly breaking the integrity of the model can surprisingly lead to improved performance.<n>Experiments show that MFT gains a consistent performance boost across various domains and backbones.
Score: 60.56962908455601
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The model is usually kept integral in the mainstream large language model (LLM) fine-tuning protocols. No works have questioned whether maintaining the integrity of the model is indispensable for performance. In this work, we introduce Mask Fine-Tuning (MFT), a brand-new LLM fine-tuning paradigm to show that properly breaking the integrity of the model can surprisingly lead to improved performance. Specifically, MFT learns a set of binary masks supervised by the typical LLM fine-tuning objective. Extensive experiments show that MFT gains a consistent performance boost across various domains and backbones (e.g., 1.95%/1.88% average gain in coding with LLaMA2-7B/3.1-8B). Detailed procedures are provided to study the proposed MFT from different hyperparameter perspectives for better insight. In particular, MFT naturally updates the current LLM training protocol by deploying it on a complete well-trained model. This study extends the functionality of mask learning from its conventional network pruning context for model compression to a more general scope.

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