This research comprehensively evaluates DeepSeek's large language models for biomedical natural language processing tasks, providing insights into their capabilities in specialized medical domains.

• Multiple DeepSeek variants tested on biomedical NLP tasks including named entity recognition and relation extraction
• Offers comparative analysis of different model sizes and architectures in specialized medical contexts
• Helps identify the most effective models for medical text analysis applications
• Provides guidance for practical implementation of LLMs in healthcare information systems

This evaluation matters for medical applications by identifying which models perform best on healthcare-specific texts, potentially improving clinical decision support, medical research analysis, and healthcare documentation systems.

Original Paper: An evaluation of DeepSeek Models in Biomedical Natural Language Processing