Generalization of Medical Large Language Models through Cross-Domain Weak Supervision
This work addresses the challenge of adapting large language models to the medical domain for real-world healthcare applications, representing an incremental advancement through a novel fine-tuning method.
The paper tackled the problem of enhancing medical large language models' generative capabilities by proposing the Incremental Curriculum-Based Fine-Tuning (ICFT) framework, which achieved consistent improvements in accuracy and efficiency across diverse medical NLP tasks compared to state-of-the-art baselines.
The advancement of large language models (LLMs) has opened new frontiers in natural language processing, particularly in specialized domains like healthcare. In this paper, we propose the Incremental Curriculum-Based Fine-Tuning (ICFT) framework to enhance the generative capabilities of medical large language models (MLLMs). ICFT combines curriculum-based learning, dual-stage memory coordination, and parameter-efficient fine-tuning to enable a progressive transition from general linguistic knowledge to strong domain-specific expertise. Experimental results across diverse medical NLP tasks, including question answering, preference classification, and response generation, demonstrate that ICFT consistently outperforms state-of-the-art baselines, achieving improvements in both accuracy and efficiency. Further analysis reveals the framework's ability to generalize to unseen data, reduce errors, and deliver diverse, contextually relevant medical responses. These findings establish ICFT as a robust and scalable solution for adapting LLMs to the medical domain, offering practical benefits for real-world healthcare applications.