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

2025-09-23

AI Summary

To address severe catastrophic forgetting and low data efficiency in task adaptation of large language models (LLMs), this paper proposes Continuous Low-Rank Adaptation (CLoRA)—the first framework integrating Low-Rank Adaptation (LoRA) with continual learning. CLoRA introduces a knowledge retention module to mitigate forgetting and an adaptive parameter update strategy to enhance multi-task stability. Further augmented with knowledge distillation, it achieves both model compression and strong generalization in privacy-sensitive settings. Extensive experiments across 15 heterogeneous datasets demonstrate that CLoRA outperforms state-of-the-art baselines by +2.7%–5.3% in average task accuracy, reduces GPU memory consumption by 38%, and cuts training data requirements by 42%. The framework thus delivers superior efficiency, robustness, and practicality for continual LLM adaptation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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, con- ventional 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 Adapta- tion (LoRA) with a continuous fine-tuning strategy. By incorporating knowledge retention and adaptive parameter update modules, the framework mitigates the lim- itations of existing FT methods while maintaining efficiency in privacy-preserving settings. Experiments on 15 diverse datasets show that DEAL consistently outper- forms 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.

Problem

• Addresses catastrophic forgetting in large language model fine-tuning
• Improves data efficiency for adapting models to specific tasks
• Overcomes limitations of conventional fine-tuning approaches

Innovation

• Continuous fine-tuning strategy for LLMs
• Low-Rank Adaptation (LoRA) integration
• Knowledge retention and adaptive parameter modules