Summary of Medg-krp: Medical Graph Knowledge Representation Probing, by Gabriel R. Rosenbaum et al.
MedG-KRP: Medical Graph Knowledge Representation Probing
by Gabriel R. Rosenbaum, Lavender Yao Jiang, Ivaxi Sheth, Jaden Stryker, Anton Alyakin, Daniel Alexander Alber, Nicolas K. Goff, Young Joon Fred Kwon, John Markert, Mustafa Nasir-Moin, Jan Moritz Niehues, Karl L. Sangwon, Eunice Yang, Eric Karl Oermann
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models have been touted as powerful tools for various medical applications due to their ability to coalesce vast amounts of information from many sources, similar to that of a human expert. However, the effectiveness of these models is often questioned, particularly when it comes to deploying them in clinical settings where accurate reasoning is paramount. To address this need for understanding, researchers have introduced a knowledge graph-based method to evaluate the biomedical reasoning abilities of large language models. This approach maps how LLMs link medical concepts to better understand their reasoning pathways. In this study, we tested GPT-4, Llama3-70b, and PalmyraMed-70b, a specialized medical model, using a panel of medical students to review the generated graphs and compare them to BIOS, a large biomedical knowledge graph. Our results show that GPT-4 performed best in our human review but worst in our ground truth comparison, while PalmyraMed performed poorly in our human review but well in our ground truth comparison. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be very smart computers that help doctors and researchers by looking at lots of information to make decisions. But before we use them in hospitals, we need to make sure they are making good decisions. To do this, scientists created a special way to test how these models think about medical problems. They looked at three different types of models: GPT-4, Llama3-70b, and PalmyraMed-70b. Students who were learning to be doctors reviewed the models’ work and compared it to a big book of medical information called BIOS. The results showed that one model was good at making decisions that sounded right to humans, but not so good when compared to the book. Another model was bad at making decisions that sounded right to humans, but good when compared to the book. |
Keywords
» Artificial intelligence » Gpt » Knowledge graph
