The paper "Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge" by Julien Delile, Srayanta Mukherjee, Anton Van Pamel, and Leonid Zhukov from the Boston Consulting Group and AI Institute addresses the challenge of retrieving and understanding the long-tail knowledge in biomedical literature. The authors highlight that while large language models (LLMs) are transforming information retrieval, they often focus on frequently seen information and neglect rare or recent discoveries, leading to an information overload problem. To overcome this, they propose a novel information-retrieval method that leverages a knowledge graph to downsample over-represented concepts and mitigate the information overload. This method outperforms traditional embedding similarity approaches in terms of precision and recall. Additionally, the authors demonstrate that combining embedding similarity with a knowledge graph can create a hybrid model that further enhances retrieval performance. The study provides insights into how knowledge graphs can improve the retrieval of long-tail biomedical knowledge and suggests potential improvements for biomedical question-answering models.The paper "Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge" by Julien Delile, Srayanta Mukherjee, Anton Van Pamel, and Leonid Zhukov from the Boston Consulting Group and AI Institute addresses the challenge of retrieving and understanding the long-tail knowledge in biomedical literature. The authors highlight that while large language models (LLMs) are transforming information retrieval, they often focus on frequently seen information and neglect rare or recent discoveries, leading to an information overload problem. To overcome this, they propose a novel information-retrieval method that leverages a knowledge graph to downsample over-represented concepts and mitigate the information overload. This method outperforms traditional embedding similarity approaches in terms of precision and recall. Additionally, the authors demonstrate that combining embedding similarity with a knowledge graph can create a hybrid model that further enhances retrieval performance. The study provides insights into how knowledge graphs can improve the retrieval of long-tail biomedical knowledge and suggests potential improvements for biomedical question-answering models.