AlpaPICO：利用大语言模型从临床试验文档中提取PICO框架
AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs
AI总结：
针对临床试验PICO框架提取依赖标注数据的问题，提出AlpaPICO框架，结合ICL无监督提取和LORA指令微调，在EBM-NLP数据集上取得有竞争力乃至当前最优的结果。
摘要：
近年来，临床试验报告的发表数量激增，使得开展系统综述颇具挑战。从临床试验研究中自动提取人群（Population）、干预措施（Intervention）、对照措施（Comparator）和结局指标（Outcome），即PICO，可缓解传统系统综述中人工审阅耗时费力的问题。现有的PICO框架提取方法多为监督式方法，依赖以BIO标签标注形式存在的人工标注数据点。近期的方法，如已被证明在多项下游自然语言处理任务中有效的上下文学习（In-Context Learning, ICL），也需要使用标注示例。在本研究中，我们采用ICL策略，利用大语言模型（Large Language Models, LLMs）在预训练阶段积累的预训练知识，在无监督设置下从临床试验文档中自动提取PICO相关术语，从而绕开对大量标注数据实例的需求。此外，为了展示大语言模型在有大量标注样本可用的理想场景下的最高效能，我们采用指令微调策略，通过低秩适配（Low Rank Adaptation, LORA）在低资源环境下对巨型模型进行训练，以完成PICO框架提取任务。我们的实验结果表明，所提出的基于ICL的框架在所有版本的EBM-NLP数据集上取得了具有竞争力的结果，而所提出的指令微调版本框架在所有不同的EBM-NLP数据集上均取得了当前最优结果。我们的项目可访问以下链接获取：https://github.com/shrimonmuke0202/AlpaPICO.git。
英文摘要：
In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging. Recent approaches, such as In-Context Learning (ICL), which has been shown to be effective for a number of downstream NLP tasks, require the use of labeled examples. In this work, we adopt ICL strategy by employing the pretrained knowledge of Large Language Models (LLMs), gathered during the pretraining phase of an LLM, to automatically extract the PICO-related terminologies from clinical trial documents in unsupervised set up to bypass the availability of large number of annotated data instances. Additionally, to showcase the highest effectiveness of LLM in oracle scenario where large number of annotated samples are available, we adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA) to conduct the training of gigantic model in low resource environment for the PICO frame extraction task. Our empirical results show that our proposed ICL-based framework produces comparable results on all the version of EBM-NLP datasets and the proposed instruction tuned version of our framework produces state-of-the-art results on all the different EBM-NLP datasets. Our project is available at \url{https://github.com/shrimonmuke0202/AlpaPICO.git}.
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。
