Summary of Guiding Clinical Reasoning with Large Language Models Via Knowledge Seeds, by Jiageng Wu et al.
Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds
by Jiageng WU, Xian Wu, Jie Yang
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed framework, In-Context Padding (ICP), aims to enhance large language models (LLMs) like ChatGPT and GPT-4 for clinical reasoning tasks. By inferring critical knowledge seeds and using them as anchors, ICP guides the generation process of LLMs to improve their ability in making accurate clinical decisions. Experimental results on two clinical question datasets show that ICP leads to significant enhancements in LLMs’ clinical reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Doctors use a thinking process called clinical reasoning to decide what tests to run, what’s wrong with patients, and what treatments are best. This process requires lots of medical knowledge and experience. In developing countries, there aren’t enough doctors or resources, which makes it harder for people to get the care they need. Recently, big computer models like ChatGPT have shown promise in helping with clinical reasoning. But these models can make mistakes and don’t think like real doctors. To fix this, researchers came up with a new way called In-Context Padding (ICP). It helps these computer models learn more medical information to make better decisions. |
Keywords
» Artificial intelligence » Gpt
