让微调模块适应更新模型，无需重训即可保持性能。
Adapt Once, Thrive with Updates: Transferable Parameter-Efficient Fine-Tuning on Evolving Base Models
- 聚焦注意力机制中的任务模式，减少对基础模型知识的依赖。
- 在7个模型、12个数据集上验证，更新后性能几乎不变。
- 适合需要频繁更新大模型的生产环境使用。
参数高效微调（PEFT）已成为微调大语言模型的常用方法，使一个基础模型可通过切换PEFT模块服务多个用户。为提升用户体验，基础模型需定期更新。然而，一旦更新，先前版本上微调的PEFT模块往往在新版本上性能大幅下降。重新微调大量模块将带来显著计算开销。通过对基础模型更新过程中的变化进行系统分析，我们发现：持续训练主要影响前馈网络（FFN）中存储的任务特定知识，而对注意力机制中的任务模式影响较小。基于此，我们提出Trans-PEFT，通过增强PEFT模块对任务模式的捕捉能力，降低其对基础模型特定知识的依赖。理论分析支持该方法。在7个基础模型和12个数据集上的广泛实验表明，经Trans-PEFT训练的模块可在不重新微调的情况下，在更新后的基础模型上保持良好性能，显著降低实际应用中的维护成本。
原文摘要 · Abstract (English)
Parameter-efficient fine-tuning (PEFT) has become a common method for fine-tuning large language models, where a base model can serve multiple users through PEFT module switching. To enhance user experience, base models require periodic updates. However, once updated, PEFT modules fine-tuned on previous versions often suffer substantial performance degradation on newer versions. Re-tuning these numerous modules to restore performance would incur significant computational costs. Through a comprehensive analysis of the changes that occur during base model updates, we uncover an interesting phenomenon: continual training primarily affects task-specific knowledge stored in Feed-Forward Networks (FFN), while having less impact on the task-specific pattern in the Attention mechanism. Based on these findings, we introduce Trans-PEFT, a novel approach that enhances the PEFT module by focusing on the task-specific pattern while reducing its dependence on certain knowledge in the base model. Further theoretical analysis supports our approach. Extensive experiments across 7 base models and 12 datasets demonstrate that Trans-PEFT trained modules can maintain performance on updated base models without re-tuning, significantly reducing maintenance overhead in real-world applications.
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