用连续低秩微调提升大模型数据效率，避免遗忘。
Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
- 结合低秩适配与连续微调，动态更新参数。
- 15个数据集上准确率显著优于基线方法。
- 适合资源有限下持续学习的场景。
大型语言模型（LLM）的微调技术在适应特定任务中起关键作用，尤其当从头训练不切实际时。传统微调方法常面临灾难性遗忘和数据效率低下的问题。本文提出DEAL框架，将低秩适配（LoRA）与连续微调策略结合，引入知识保留和自适应参数更新模块，有效缓解现有方法缺陷。在15个不同数据集上的实验表明，DEAL始终优于基线方法，在任务准确率和资源效率方面均有显著提升。结果证明该方法可推动大模型持续适配，实现更高性能与更优资源利用。代码已开源：https://github.com/zzm-black/DEAL-Continuous-Low-Rank-Fine-Tuning。
原文摘要 · Abstract (English)
Recent advancements in Large Language Models (LLMs) have emphasized the critical role of fine-tuning (FT) techniques in adapting LLMs to specific tasks, especially when retraining from scratch is computationally infeasible. Fine-tuning enables LLMs to leverage task- or domain-specific data, producing models that more effectively meet the requirements of targeted applications. However, conventional FT approaches often suffer from catastrophic forgetting and suboptimal data efficiency, limiting their real-world applicability. To address these challenges, this paper proposes \textbf{DEAL}, a novel framework that integrates Low-Rank Adaptation (LoRA) with a continuous fine-tuning strategy. By incorporating knowledge retention and adaptive parameter update modules, the framework mitigates the limitations of existing FT methods while maintaining efficiency. Experiments on 15 diverse datasets show that DEAL consistently outperforms baseline methods, yielding substantial gains in task accuracy and resource efficiency. These findings demonstrate the potential of our approach to advance continual adaptation in LLMs by enhancing task performance while improving resource efficiency. The source code is publicly available at https://github.com/zzm-black/DEAL-Continuous-Low-Rank-Fine-Tuning.
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