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Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning

Xiao Han, Zimo Zhao, Wanyu Wang, Maolin Wang, Zitao Liu, Yi Chang, Xiangyu Zhao

TL;DR

DEAL, a novel framework that integrates Low-Rank Adaptation (LoRA) with a continuous fine-tuning strategy that incorporates knowledge retention and adaptive parameter update modules, mitigates the limitations of existing FT methods while maintaining efficiency.

Abstract

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.

Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning

TL;DR

DEAL, a novel framework that integrates Low-Rank Adaptation (LoRA) with a continuous fine-tuning strategy that incorporates knowledge retention and adaptive parameter update modules, mitigates the limitations of existing FT methods while maintaining efficiency.

Abstract

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.

Paper Structure

This paper contains 38 sections, 1 theorem, 17 equations, 3 figures, 10 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Methodology
  4. Framework Overview
  5. Wavelet Kernel-based Knowledge Retention
  6. Controlled Knowledge Updating
  7. Training Procedure
  8. Experiments
  9. Datasets.
  10. Implementation Details.
  11. Evaluation Metrics.
  12. Main Results
  13. Ablation Studies
  14. Adapter Update Strategy.
  15. LoRA Rank.
  16. ...and 23 more sections

Key Result

Theorem 1

Let $\boldsymbol{Y}$ be the observed data matrix and $\boldsymbol{X}$ the underlying core feature matrix. Then, without additional constraints, there does not exist a pair of matrices $\boldsymbol{P}_1$ and $\boldsymbol{U}_{x1}$ such that $\boldsymbol{P}_1 = \boldsymbol{U}_{x1}$. See Appendix app:th

Figures (3)

  • Figure 1: The framework overview.
  • Figure 2: DEAL ablations: (a) adapter update strategy, (b) task-order robustness on the 4-task benchmark, (c) LoRA-rank sensitivity on the 3-task benchmark.
  • Figure 3: Two case studies demonstrating continual learning and knowledge retention across classification tasks.

Theorems & Definitions (1)

  • Theorem 1