Adapting Biomedical Abstracts into Plain language using Large Language Models

• URL: http://arxiv.org/abs/2501.15700v1
• Date: Sun, 26 Jan 2025 23:07:35 GMT
• Title: Adapting Biomedical Abstracts into Plain language using Large Language Models
• Authors: Haritha Gangavarapu, Giridhar Kaushik Ramachandran, Kevin Lybarger, Meliha Yetisgen, Özlem Uzuner,
• Abstract summary: The goal of the Plain Language Adaptation of Biomedical Abstracts (PLABA) track is to adapt the biomedical abstracts in English language extracted from PubMed based on the questions asked in MedlinePlus for the general public using plain language at the sentence level. We leveraged the best open-source Large Language Models suitable and fine-tuned for dialog use cases. Our top performing GPT-4 based model ranked first in the avg. simplicity measure and 3rd on the avg. accuracy measure.
• Score: 8.497188292342052
• License:
• Abstract: A vast amount of medical knowledge is available for public use through online health forums, and question-answering platforms on social media. The majority of the population in the United States doesn't have the right amount of health literacy to make the best use of that information. Health literacy means the ability to obtain and comprehend the basic health information to make appropriate health decisions. To build the bridge between this gap, organizations advocate adapting this medical knowledge into plain language. Building robust systems to automate the adaptations helps both medical and non-medical professionals best leverage the available information online. The goal of the Plain Language Adaptation of Biomedical Abstracts (PLABA) track is to adapt the biomedical abstracts in English language extracted from PubMed based on the questions asked in MedlinePlus for the general public using plain language at the sentence level. As part of this track, we leveraged the best open-source Large Language Models suitable and fine-tuned for dialog use cases. We compare and present the results for all of our systems and our ranking among the other participants' submissions. Our top performing GPT-4 based model ranked first in the avg. simplicity measure and 3rd on the avg. accuracy measure.

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