Advancements in Large Language Models for Biomedical Reasoning

The field of biomedical natural language processing is witnessing significant advancements with the integration of large language models (LLMs) and knowledge graphs. Recent developments are focused on improving the reasoning capabilities of LLMs over knowledge graphs, enabling them to better capture complex relationships and provide more accurate results. Notable innovations include the use of super-relations to enhance forward and backward reasoning, retrieval-augmented knowledge mining methods, and the development of large-scale medical reasoning datasets. These advancements are paving the way for more accurate and reliable biomedical question answering, medical diagnosis, and treatment planning.

Some noteworthy papers in this area include: The ReKnoS framework, which introduces the concept of super-relations to improve retrieval efficiency and reasoning performance. The MedReason dataset, which provides detailed, step-by-step explanations for medical question answering and has been shown to significantly improve medical problem-solving capabilities. The GMAI-VL-R1 model, which harnesses reinforcement learning to improve multimodal medical reasoning and has demonstrated excellent performance in tasks such as medical image diagnosis and visual question answering.

Sources

Reasoning of Large Language Models over Knowledge Graphs with Super-Relations

Can LLMs Support Medical Knowledge Imputation? An Evaluation-Based Perspective

A Retrieval-Augmented Knowledge Mining Method with Deep Thinking LLMs for Biomedical Research and Clinical Support

WHERE and WHICH: Iterative Debate for Biomedical Synthetic Data Augmentation

MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs

Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph

GMAI-VL-R1: Harnessing Reinforcement Learning for Multimodal Medical Reasoning

Affordable AI Assistants with Knowledge Graph of Thoughts

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