Large Language Models Encode Clinical Knowledge
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ready to break down some research. This paper, Large Language Models in Code Clinical Knowledge, explores how well large language models, LLMs, can understand and answer medical questions. It's a pretty relevant topic, especially with the growing interest in AI in health care. Oh, absolutely. It's like, can these

super smart language models like the ones that power chat bots and write articles actually be useful in a medical setting. That's a pretty big deal. Exactly. And the paper introduces this new benchmark called MultiMed QA to test these models. Could you tell us more about that? Right. MultiMed QA is this super

super cool benchmark that combines a bunch of different medical question answering data sets. Some are from professional medical exams, some are from research papers, and some are even questions that people search for online. It's like a big test to see how well these models understand different types of medical questions.

That's interesting. And I see they tested a model called FlonPalm on this benchmark. How did it do? FlonPalm did exceptionally well on the multiple choice questions, achieving state-of-the-art accuracy on all of them, even beating out some other really strong models. On MedQA, which is like a test for doctors in the U.S., it beat the previous best model by over 17%. Wow, that does sound impressive.

But I guess multiple choice questions are just one part of the picture. What about more open-ended questions, like the ones people might ask their doctor? The paper mentions that Flan Palm's performance on open-ended questions revealed some gaps that need to be addressed. That makes sense. Medicine is a complex field, and there is a high bar for safety. So how did the researchers try to address this issue? They came up with this clever technique called instruction prompt tuning.

Basically, it's a way to fine-tune the model with just a few examples of good medical answers. It's like giving the model a crash course in how to give safe and helpful medical advice. The model that came out of this tuning was called MedPalm. MedPalm, huh? And did that improve things? It did. MedPalm's answers were a lot better than FlanPalm's. They were more in line with what doctors would say and less likely to be harmful.

It's a really cool example of how we can make these models safer and more useful for medical applications. That's reassuring to hear, but I imagine there's still a lot of work to be done before we can trust these models in a real clinical setting, right? Oh yeah, for sure. This paper is just a first step.

We need to develop better ways to evaluate these models, especially when it comes to things like bias and fairness. And we need to keep fine tuning them to make sure they're giving accurate and helpful information to everyone. Definitely.

Sounds like there's a lot of exciting potential here, but also a lot of responsibility to get it right. No doubt about it. This paper really highlights both the promise and the challenges of using LLMs in medicine. It's a super cool field, and I'm stoked to see where it goes from here. Thank you for discussing this.