  Large Language Models (LLMs) have swiftly emerged as vital resources for
different applications in the biomedical and healthcare domains; however, these
models encounter issues such as generating inaccurate information or
hallucinations. Retrieval-augmented generation provided a solution for these
models to update knowledge and enhance their performance. In contrast to
previous retrieval-augmented LMs, which utilize specialized cross-attention
mechanisms to help LLM encode retrieved text, BiomedRAG adopts a simpler
approach by directly inputting the retrieved chunk-based documents into the
LLM. This straightforward design is easily applicable to existing retrieval and
language models, effectively bypassing noise information in retrieved
documents, particularly in noise-intensive tasks. Moreover, we demonstrate the
potential for utilizing the LLM to supervise the retrieval model in the
biomedical domain, enabling it to retrieve the document that assists the LM in
improving its predictions. Our experiments reveal that with the tuned
scorer,\textsc{ BiomedRAG} attains superior performance across 5 biomedical NLP
tasks, encompassing information extraction (triple extraction, relation
extraction), text classification, link prediction, and question-answering,
leveraging over 9 datasets. For instance, in the triple extraction task,
\textsc{BiomedRAG} outperforms other triple extraction systems with micro-F1
scores of 81.42 and 88.83 on GIT and ChemProt corpora, respectively.
