Prompt-Aware Continual Learning for Catastrophic Forgetting Mitigation in Large Language Models
Keywords:
continual learning, catastrophic forgetting, large language models, prompt tuning, system architecture, governance, fairness, sustainabilityAbstract
Large language models (LLMs) have achieved remarkable generalization across diverse tasks, yet their deployment in dynamically evolving environments exposes a critical vulnerability: catastrophic forgetting when sequentially updated with new knowledge. Traditional fine-tuning overwrites previously acquired capabilities, rendering continuous adaptation impractical for long-lived systems. This paper proposes a prompt-aware continual learning paradigm that mitigates forgetting by treating discrete and continuous prompts as persistent, task-specific memory modules. Rather than modifying model parameters, the backbone remains frozen while a prompt selection mechanism dynamically retrieves contextually appropriate prompt embeddings. We provide a system-level analysis of the architectural trade-offs, infrastructure requirements, and governance implications of this approach. The discussion encompasses prompt storage architectures, retrieval latency, versioning, and load balancing for large-scale deployment. We examine fairness concerns arising from prompt accumulation, particularly the risk of performance degradation for underrepresented tasks and the propagation of prompt-induced biases. Policy dimensions including auditability, ownership of prompt modules, and compliance with emerging AI regulations are addressed. Furthermore, we compare prompt-aware continual learning with rehearsal, regularization, and dynamic expansion strategies, highlighting advantages in modularity and energy efficiency. Robustness to distribution shift, prompt collision, and long-term maintainability are explored through the lens of sustainable model lifecycle management. The paper concludes with forward-looking perspectives on integrating prompt-aware continual learning into multi-tenant AI platforms, emphasizing the need for standardization, monitoring, and inclusive governance frameworks to ensure responsible, equitable, and resilient LLM evolution.
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