LLMs真的多语言吗？探索LLMs在信息检索中的零样本多语言能力：一个意大利医疗应用案例
Are LLMs Truly Multilingual? Exploring Zero-Shot Multilingual Capability of LLMs for Information Retrieval: An Italian Healthcare Use Case
AI总结：
本文探讨了开源多语言LLMs在零样本条件下处理意大利语电子健康记录信息提取的能力，发现部分模型在泛化能力上存在不足。
摘要：
大型语言模型（LLMs）已成为人工智能和自然语言处理领域的重要话题，通过提升客户服务、自动化任务、提供洞察、改进诊断和个性化学习体验，正在改变医疗、金融、教育和营销等行业。从临床记录中提取信息是数字医疗中的关键任务。尽管过去传统NLP技术已用于此，但由于临床语言的复杂性、变异性以及自由文本中的高内在语义，往往效果不佳。最近，大型语言模型（LLMs）已成为更好地理解和生成人类样文本的有力工具，使其在这一领域非常有效。在本文中，我们探讨了开源多语言LLMs理解意大利语电子健康记录（EHRs）的能力，并帮助实时从中提取信息。我们的详细实验研究显示，某些LLMs在零样本、本地设置中表现不佳，而其他模型在性能上存在显著差异，难以在各种疾病之间泛化，与原生模式匹配和手动注释相比表现较差。
英文摘要：
Large Language Models (LLMs) have become a key topic in AI and NLP, transforming sectors like healthcare, finance, education, and marketing by improving customer service, automating tasks, providing insights, improving diagnostics, and personalizing learning experiences. Information extraction from clinical records is a crucial task in digital healthcare. Although traditional NLP techniques have been used for this in the past, they often fall short due to the complexity, variability of clinical language, and high inner semantics in the free clinical text. Recently, Large Language Models (LLMs) have become a powerful tool for better understanding and generating human-like text, making them highly effective in this area. In this paper, we explore the ability of open-source multilingual LLMs to understand EHRs (Electronic Health Records) in Italian and help extract information from them in real-time. Our detailed experimental campaign on comorbidity extraction from EHR reveals that some LLMs struggle in zero-shot, on-premises settings, and others show significant variation in performance, struggling to generalize across various diseases when compared to native pattern matching and manual annotations.
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。
