CPEL: A Causality-Aware, Parameter-Efficient Learning Framework for Adaptation of Large Language Models with Case Studies in Geriatric Care and Beyond

Adapting Large Language Models (LLMs) to specialized domains like geriatric care remains a significant challenge due to the limited availability of domain-specific data and the difficulty of achieving efficient yet effective fine-tuning. Current methods often fail to effectively harness domain-specific causal insights, which are crucial for understanding and solving complex problems in low-resource domains.To address these challenges, we propose Causality-Aware, Parameter-Efficient Learning (CPEL), a novel framework that leverages domain-specific causal relationships to guide a multi-layer, parameter-efficient fine-tuning process for more effective domain adaptation. By embedding causal reasoning into the model’s adaptation pipeline, CPEL enables efficient specialization in the target domain while maintaining strong task-specific performance. Specifically, the Causal Prompt Generator of CPEL extracts and applies domain-specific causal structures, generating adaptive prompts that effectively guide the model’s learning process. Complementing this, the MPEFT module employs a dual-adapter mechanism to balance domain-level adaptation with downstream task optimization. This cohesive design ensures that CPEL achieves resource efficiency while capturing domain knowledge in a structured and interpretable manner. Based on this framework, we delved into its application in the field of geriatric care and trained a specialized large language model (Geriatric Care LLaMA) tailored for the aged-care domain, leveraging its capacity to efficiently integrate domain expertise. Experimental results from question-answering tasks demonstrate that CPEL improves ROUGE scores by 9–14% compared to mainstream LLMs and outperforms frontier models by 1–2 points in auto-scoring tasks. In summary, CPEL demonstrates robust generalization and cross-domain adaptability, highlighting its scalability and effectiveness as a transformative solution for domain adaptation in specialized, resource-constrained fields.