Click MedQSum
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README.md
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## MedQSum
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<img src="https://raw.githubusercontent.com/zekaouinoureddine/MedQSum/master/assets/models.png" alt="drawing" width="600"/>
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## TL;DR
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**medqsum-bart-large-xsum-meqsum** is the best fine-tuned model in the paper [Enhancing Large Language Models' Utility for Medical Question-Answering: A Patient Health Question Summarization Approach](), which introduces a solution to get the most out of LLMs, when answering health-related questions. We address the challenge of crafting accurate prompts by summarizing consumer health questions (CHQs) to generate clear and concise medical questions. Our approach involves fine-tuning Transformer-based models, including Flan-T5 in resource-constrained environments and three medical question summarization datasets.
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## MedQSum
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<a href="https://github.com/zekaouinoureddine/MedQSum">
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<img src="https://raw.githubusercontent.com/zekaouinoureddine/MedQSum/master/assets/models.png" alt="drawing" width="600"/>
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</a>
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## TL;DR
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**medqsum-bart-large-xsum-meqsum** is the best fine-tuned model in the paper [Enhancing Large Language Models' Utility for Medical Question-Answering: A Patient Health Question Summarization Approach](), which introduces a solution to get the most out of LLMs, when answering health-related questions. We address the challenge of crafting accurate prompts by summarizing consumer health questions (CHQs) to generate clear and concise medical questions. Our approach involves fine-tuning Transformer-based models, including Flan-T5 in resource-constrained environments and three medical question summarization datasets.
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