Summary of Xai4llm. Let Machine Learning Models and Llms Collaborate For Enhanced In-context Learning in Healthcare, by Fatemeh Nazary et al.

XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced In-Context Learning in Healthcare

by Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia, Eugenio di Sciascio

First submitted to arxiv on: 10 May 2024

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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 This study develops a novel method for integrating Large Language Models into healthcare diagnostics. The approach involves using structured prompts to integrate medical domain knowledge, enabling zero-shot/few-shot in-context learning. Two communication styles are explored: Numerical Conversational (NC) and Natural Language Single-Turn (NL-ST). The NC style processes data incrementally, while the NL-ST style uses long narrative prompts.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This study helps doctors use computers to make better medical decisions. It creates a new way for Large Language Models to learn from medical information without needing lots of training examples. This is done by using special prompts that help the models understand medical concepts. The researchers test two different ways of talking to the models: one that gives small bits of information at a time, and another that uses long stories.

Keywords

» Artificial intelligence » Few shot » Zero shot

XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced In-Context Learning in Healthcare

by Fatemeh Nazary, Yashar Deldjoo, Tommaso Di Noia, Eugenio di Sciascio

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GrooveSquid.com Paper Summaries

Keywords

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