Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
This addresses data efficiency and forgetting issues in adapting LLMs for specific tasks, representing an incremental improvement over existing fine-tuning methods.
The paper tackles the problem of catastrophic forgetting and suboptimal data efficiency in fine-tuning large language models by proposing DEAL, a framework integrating Low-Rank Adaptation with continuous fine-tuning, which outperforms baselines on 15 datasets with gains in accuracy and resource efficiency.
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.