Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
Xiao Han, Zhao Zimo, Wanyu Wang, Maolin Wang, Zitao Liu, Yi Chang, Xiangyu Zhao
Recent advancements in Large Language Models (LLMs) have emphasized the critical role of fine-tuning (FT) techniques in adapting LLMs to specific tasks, especially in privacy-sensitive scenarios.Users can leverage this technology by incorporating their personal data to tailor a unique LLM that perfectly aligns with their needs.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 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 in privacy-preserving settings.Experiments on  \textbf{15} diverse datasets show that DEAL consistently outperforms baseline methods, yielding substantial gains in task accuracy and resource efficiency. These results underscore the potential of the proposed approach to improve model adaptability while safeguarding data privacy. The source code is publicly available at https://anonymous.4open.science/r/DEAL-Continuous-Low-Rank-Fine-Tuning-058A