MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning

• URL: http://arxiv.org/abs/2407.20999v2
• Date: Wed, 31 Jul 2024 17:56:03 GMT
• Title: MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning
• Authors: Yupeng Chen, Senmiao Wang, Zhihang Lin, Zeyu Qin, Yushun Zhang, Tian Ding, Ruoyu Sun,
• Abstract summary: During fine-tuning, large language models (LLMs) may forget the knowledge acquired in the pre-training stage, leading to a decline in general capabilities. We propose a new fine-tuning algorithm termed Momentum-Filtered algorithm (MoFO) MoFO achieves similar fine-tuning performance while keeping parameters closer to the pre-trained model.
• Score: 11.174544614042984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks. Typically, an LLM is pre-trained on large corpora and subsequently fine-tuned on task-specific datasets. However, during fine-tuning, LLMs may forget the knowledge acquired in the pre-training stage, leading to a decline in general capabilities. To address this issue, we propose a new fine-tuning algorithm termed Momentum-Filtered Optimizer (MoFO). The key idea of MoFO is to iteratively select and update the model parameters with the largest momentum magnitudes. Compared to full-parameter training, MoFO achieves similar fine-tuning performance while keeping parameters closer to the pre-trained model, thereby mitigating knowledge forgetting. Unlike most existing methods for forgetting mitigation, MoFO combines the following two advantages. First, MoFO does not require access to pre-training data. This makes MoFO particularly suitable for fine-tuning scenarios where pre-training data is unavailable, such as fine-tuning checkpoint-only open-source LLMs. Second, MoFO does not alter the original loss function. This could avoid impairing the model performance on the fine-tuning tasks. We validate MoFO through rigorous convergence analysis and extensive experiments, demonstrating its superiority over existing methods in mitigating forgetting and enhancing fine-tuning performance.

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