Continual Learning of Large Language Models: A Comprehensive Survey

The evolution of NLP has seen the advent of massive models like GPT-3, BERT, and LLaMA, which demonstrate remarkable capabilities in understanding and generating language. These models are trained on enormous datasets, often spanning diverse domains, leading to emergent abilities such as few-shot learning and multi-step reasoning. However, static training approaches face limitations: knowledge becomes outdated, and models struggle to adapt to new tasks without forgetting previous ones. Early efforts focused on transfer learning and fine-tuning, but these methods are insufficient for lifelong learning scenarios. Recent research emphasizes continual learning techniques—such as replay buffers, regularization, and architecture expansion—to enable models to learn sequentially without catastrophic forgetting. Despite progress, challenges remain in scaling these methods, evaluating their effectiveness, and ensuring stability across diverse tasks and domains.

The core issue addressed is catastrophic forgetting—when models lose previously learned knowledge after training on new tasks—especially in large-scale models. This problem hampers the deployment of AI systems in real-world, dynamic environments where continuous updates are necessary. Existing solutions like replay buffers and regularization have limitations in scalability and effectiveness, particularly with models containing billions of parameters. Additionally, there is a lack of unified evaluation standards to measure knowledge retention, transfer, and stability across tasks. The challenge is to develop methods that balance plasticity (learning new information) and stability (retaining old knowledge), ensuring models can adapt efficiently without significant performance degradation.

The paper introduces a multi-dimensional framework that unifies vertical (hierarchical task progression) and horizontal (temporal and domain shifts) continual learning. It proposes a staged training process—CPT, DAP, and CFT—integrating techniques like replay buffers, Elastic Weight Consolidation (EWC), and LoRA for parameter-efficient adaptation. This approach allows models to retain general knowledge while specializing in domains, and adapt over time without catastrophic forgetting. The framework emphasizes multi-task and multi-domain scenarios, providing a comprehensive strategy for scalable, real-world continual learning. It also introduces standardized evaluation protocols, including metrics like average performance, forgetting rate, and transferability, to benchmark progress systematically.

• �� Pre-training: Train a large, general-purpose model on diverse datasets (e.g., Common Crawl, Wikipedia).• Domain-Adaptive Pre-training: Fine-tune on domain-specific data (e.g., PubMed, financial texts) using techniques like masked language modeling (MLM) or autoregressive training.• Continual Fine-tuning: Sequentially adapt the model to new tasks, employing replay buffers to retain old data samples, regularization methods like EWC to prevent parameter drift, and architecture expansion (e.g., LoRA) for parameter efficiency.• Evaluation: Use metrics such as average accuracy, forgetting rate, and forward transfer across benchmarks like SQuAD, BioASQ, and GLUE.• Ablation: Test each component’s contribution by removing or replacing techniques, analyzing performance impacts to optimize the framework.

Experiments involve multiple NLP tasks, including question answering, sentiment analysis, and domain-specific tasks in medicine and finance. Models like GPT-3 and LLaMA serve as baselines, with modifications incorporating replay buffers, EWC, and LoRA. Performance is measured on datasets such as SQuAD, BioASQ, and GLUE, focusing on accuracy, forgetting rate, and transferability. Hyperparameters include buffer size, regularization strength, and learning rates, optimized via grid search. Ablation studies compare different combinations of techniques, confirming that integrated methods outperform single approaches by at least 15%. Results demonstrate improved knowledge retention, domain adaptation, and efficiency, validating the framework’s effectiveness.

The combined approach achieves over 15% improvement in average task performance, reduces forgetting by 30%, and enhances domain-specific accuracy by 20%. LoRA-based architecture expansion maintains parameter efficiency, with a 20% reduction in training costs. In medical and financial tasks, models outperform baselines significantly, while preserving core general knowledge. Ablation results show that replay buffers and regularization synergistically improve stability, and architecture expansion accelerates adaptation. These findings confirm that multi-technique integration is essential for scalable continual learning in large models.

This framework supports real-time knowledge updates in sectors like healthcare, finance, and legal services, enabling models to adapt to evolving data streams without retraining from scratch. It facilitates deployment of AI assistants, automated reporting, and personalized recommendations that remain current and reliable. The approach also benefits research in multi-task learning, domain adaptation, and AI safety, fostering models that learn continuously with minimal performance loss. Future integration with multi-modal data and reinforcement learning could further expand its industrial relevance.

Current methods face scalability issues with extremely large models and multi-modal data, limiting robustness in complex scenarios. Architecture expansion techniques like LoRA, while parameter-efficient, still incur high computational costs at scale. Evaluation protocols lack standardization, making cross-study comparisons difficult. Additionally, the stability-plasticity trade-off remains challenging, especially in highly dynamic environments. Future work must address these limitations by developing more scalable, efficient, and standardized continual learning methods, and ensuring model interpretability and safety in lifelong learning contexts.