BiomedRAG is a retrieval-augmented large language model designed for biomedical applications. It improves performance by directly inputting retrieved chunk-based documents into the LLM, bypassing noise and enhancing accuracy. The model uses a tailored chunk scorer to select relevant documents from a diverse chunk database, which are then integrated into the LLM's input to generate outputs like structured knowledge. BiomedRAG outperforms existing models in four biomedical NLP tasks, including information extraction, text classification, and link prediction, across eight datasets. It achieves high micro-F1 scores in triple extraction tasks, demonstrating its effectiveness in biomedical intervention systems. The model's performance is validated through extensive experiments, showing significant improvements over strong baseline models. BiomedRAG's key contributions include a novel retrieval-augmented framework, a learnable chunk scorer, and validation on four biomedical NLP tasks with eight datasets. The model also demonstrates robustness in handling noise-intensive tasks and improves performance by reducing training time and addressing input length limitations. The framework is evaluated on various biomedical datasets, including GIT, DDI, ChemProt, and others, and shows superior performance compared to existing models. The model's effectiveness is supported by experimental results and ablation studies, highlighting its potential in biomedical NLP tasks.BiomedRAG is a retrieval-augmented large language model designed for biomedical applications. It improves performance by directly inputting retrieved chunk-based documents into the LLM, bypassing noise and enhancing accuracy. The model uses a tailored chunk scorer to select relevant documents from a diverse chunk database, which are then integrated into the LLM's input to generate outputs like structured knowledge. BiomedRAG outperforms existing models in four biomedical NLP tasks, including information extraction, text classification, and link prediction, across eight datasets. It achieves high micro-F1 scores in triple extraction tasks, demonstrating its effectiveness in biomedical intervention systems. The model's performance is validated through extensive experiments, showing significant improvements over strong baseline models. BiomedRAG's key contributions include a novel retrieval-augmented framework, a learnable chunk scorer, and validation on four biomedical NLP tasks with eight datasets. The model also demonstrates robustness in handling noise-intensive tasks and improves performance by reducing training time and addressing input length limitations. The framework is evaluated on various biomedical datasets, including GIT, DDI, ChemProt, and others, and shows superior performance compared to existing models. The model's effectiveness is supported by experimental results and ablation studies, highlighting its potential in biomedical NLP tasks.