How can LLMs (Large Language Models) be ... - tlooto, The Most Powerful AcademicGPT

How can LLMs (Large Language Models) be fine-tuned for domain-specific tasks in healthcare? Large Language Models (LLMs) can be fine-tuned for domain-specific tasks in healthcare by leveraging several specialized strategies that account for the unique linguistic, data, and operational requirements of medical environments. Fine-tuning typically involves adapting a general-purpose LLM to healthcare-specific downstream tasks, such as named entity recognition (NER), medical question-answering, clinical text summarization, or concept normalization. This can be accomplished through supervised fine-tuning on domain-specific datasets, instruction tuning, reinforcement learning from human feedback, and parameter-efficient adaptation methods [1][2][3][4][5][6]. A central approach is continual pre-training or further training of a base LLM on large-scale, domain-specific corpora—such as electronic health records, biomedical literature, or anonymized clinical narratives—which enables the model to