基于大语言模型角色扮演的个性化医学术语提取
Large Language Model-based Role-Playing for Personalized Medical Jargon Extraction
AI总结：
本文首次定量验证大语言模型角色扮演在个性化医学术语提取中的效果，证明其可提升F1分数并超越此前最优模型，从而改进个性化患者教育。
摘要：
此前研究表明，电子健康记录（EHR）已在美国广泛采用，使患者能够访问其个人医疗信息，但由于医学术语普遍存在，其对患者的可读性并不高。通过识别对每个人而言难以理解的术语，使医疗记录适应个人理解能力，将增强生成模型的效用。我们提出了首个定量分析，以衡量角色扮演在大语言模型（LLM）医学词提取中的影响。通过比较 Mechanical Turk 工作人员在 20 个句子上的结果，我们的研究表明，在 14 种不同社会人口背景下，LLM 角色扮演在 95% 的情况下提高了 F1 分数。此外，将角色扮演与上下文学习相结合，其表现优于此前最先进的模型。我们的研究表明，ChatGPT 可以通过利用角色扮演来提供个性化患者教育，从而改进传统医学词提取系统，这是此前模型未能实现的潜力。
英文摘要：
Previous studies reveal that Electronic Health Records (EHR), which have been widely adopted in the U.S. to allow patients to access their personal medical information, do not have high readability to patients due to the prevalence of medical jargon. Tailoring medical notes to individual comprehension by identifying jargon that is difficult for each person will enhance the utility of generative models. We present the first quantitative analysis to measure the impact of role-playing in LLM in medical term extraction. By comparing the results of Mechanical Turk workers over 20 sentences, our study demonstrates that LLM role-playing improves F1 scores in 95% of cases across 14 different socio-demographic backgrounds. Furthermore, applying role-playing with in-context learning outperformed the previous state-of-the-art models. Our research showed that ChatGPT can improve traditional medical term extraction systems by utilizing role-play to deliver personalized patient education, a potential that previous models had not achieved.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
