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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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多面性自然语言处理基于任务评估的双向编码器表示从变压器模型的双语 (韩语和英语) 临床说明:算法开发和验证.

Kyungmo Kim¹, Seongkeun Park², Jeongwon Min¹

¹Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, Republic of Korea.

JMIR medical informatics

|October 30, 2024

概括

这项研究比较了来自变压器的双向编码器表示 (BERT) 模型的韩语和英语临床笔记. 多语种BERT (M-BERT) 在阅读理解和知识推断任务中表现优异.

关键词:

在NLP中,我们使用了NLP.大型语言模型.自然语言推理推理自然语言推理.自然语言处理自然语言处理.阅读理解阅读理解变压器的变压器是一个变压器.

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科学领域:

自然语言处理自然语言处理.
临床信息学临床信息学
人工智能的人工智能

背景情况:

变压器双向编码器表示 (BERT) 模型越来越多地用于临床应用,如患者分类.
目前的研究往往忽视了对非英语医学文件中的BERT模型的彻底临床上下文理解评估和比较研究.
英语培训的BERT模型在非英语临床环境中的适用性仍未得到证实.

研究的目的:

评估各种BERT模型的上下文理解能力,应用于混合韩语和英语临床笔记.
确定非英语临床笔记最有效的BERT模型.

主要方法:

预先训练的BERT基础,生物BERT (生物医学文本挖掘的BERT),KoBERT (韩国BERT) 和多语言BERT (M-BERT) 使用来自164,460名患者的数据.
对比了7个微调任务中的模型性能,以评估上下文理解.

主要成果:

在分类任务中,BERT-base和BioBERT表现出色 (最高F1得分为89.32),即使使用有限的韩国代币,也表现出有效性.
多语言BERT (M-BERT) 在阅读理解 (F1分数93.77) 和知识推断 (hit@10分数95.41) 中表现出色.
模型性能因任务和代币使用而异,少一些未知代币通常会产生更好的结果.

结论:

不同的BERT模型在多语言临床领域显示出有效性.
结果为在临床和基于语言的应用中选择合适的BERT模型提供了参考.
进一步的研究可以探索针对特定非英语临床任务的最佳BERT模型选择.