用大模型将专业医学摘要转为普通人能懂的通俗语言。
Adapting Biomedical Abstracts into Plain language using Large Language Models
- 基于GPT-4等开源大模型，针对公众提问优化医学摘要的平易化表达。
- 在通俗性指标上排名第一，在准确性上位列第三，表现领先。
- 适合医疗科普、健康信息传播及非专业人士快速理解医学内容。
大量医学知识通过在线健康论坛和社交媒体问答平台向公众开放，但美国多数民众缺乏足够的健康素养，难以有效利用这些信息。健康素养指获取并理解基本健康信息以做出恰当健康决策的能力。为弥合这一差距，需将医学知识转化为通俗语言。自动化系统可帮助医患及非专业人士更好利用网络信息。本研究参与了“生物医学摘要通俗化”（PLABA）任务，目标是将来自PubMed的英文医学摘要，根据MedlinePlus中公众提问的内容，转换为通俗易懂的句子级表达。我们采用最适合对话场景的开源大模型进行微调，并对比分析所有系统表现。最终，基于GPT-4的模型在平均通俗性指标上排名第一，在平均准确率指标上位列第三。
原文摘要 · Abstract (English)
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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