  Large language models (LLMs) are transforming the way information is
retrieved with vast amounts of knowledge being summarized and presented via
natural language conversations. Yet, LLMs are prone to highlight the most
frequently seen pieces of information from the training set and to neglect the
rare ones. In the field of biomedical research, latest discoveries are key to
academic and industrial actors and are obscured by the abundance of an
ever-increasing literature corpus (the information overload problem). Surfacing
new associations between biomedical entities, e.g., drugs, genes, diseases,
with LLMs becomes a challenge of capturing the long-tail knowledge of the
biomedical scientific production. To overcome this challenge, Retrieval
Augmented Generation (RAG) has been proposed to alleviate some of the
shortcomings of LLMs by augmenting the prompts with context retrieved from
external datasets. RAG methods typically select the context via maximum
similarity search over text embeddings. In this study, we show that RAG methods
leave out a significant proportion of relevant information due to clusters of
over-represented concepts in the biomedical literature. We introduce a novel
information-retrieval method that leverages a knowledge graph to downsample
these clusters and mitigate the information overload problem. Its retrieval
performance is about twice better than embedding similarity alternatives on
both precision and recall. Finally, we demonstrate that both embedding
similarity and knowledge graph retrieval methods can be advantageously combined
into a hybrid model that outperforms both, enabling potential improvements to
biomedical question-answering models.
