ARETE：一个利用大语言模型从文本中自动检索的R包
ARETE: an R package for Automated REtrieval from TExt with large language models
AI总结：
本文提出ARETE R包，利用chatGPT API自动化从文献中提取物种出现记录，并在100种蜘蛛数据上验证其能将已知出现范围平均扩大三个数量级，为保护规划和灭绝风险评估提供关键数据支持。
摘要：
1. 实施严格保护举措的一个硬性障碍是我们缺乏关键物种数据，尤其是出现记录数据。此外，由于人类活动的影响，研究人员不得不应对新信息必须被加速收集和处理的问题。从科学论文到灰色文献的各种出版物中都包含这些关键信息，但其数据往往无法机器读取，需要大量人工工作才能提取。2. 我们介绍ARETE R包，这是一个开源软件，旨在利用大语言模型（即使用chatGPT应用程序编程接口）自动化物种出现记录的数据提取。该R包集成了数据提取与验证过程的所有步骤，从光学字符识别到异常值检测，再到表格格式输出。此外，我们通过系统比较模型输出与人工标注者的工作来验证ARETE。3. 我们通过比较使用GBIF数据生成的范围图与对100种蜘蛛自动提取的范围图，证明了该方法的实用性。新提取的数据使已知的出现范围平均扩大了三个数量级，揭示了物种过去被发现的新区域，这可能对空间保护规划和灭绝风险评估具有重要意义。4. ARETE能够更快地获取迄今尚未开发的出现记录数据，在需要此类数据的项目中可能改变游戏规则。研究人员将能够更好地优先分配资源，手动验证选定物种，同时对大多数物种保持自动提取。该工作流程还允许在项目规划期间预测可用的文献数据。
英文摘要：
1. A hard stop for the implementation of rigorous conservation initiatives is our lack of key species data, especially occurrence data. Furthermore, researchers have to contend with an accelerated speed at which new information must be collected and processed due to anthropogenic activity. Publications ranging from scientific papers to gray literature contain this crucial information but their data are often not machine-readable, requiring extensive human work to be retrieved. 2. We present the ARETE R package, an open-source software aiming to automate data extraction of species occurrences powered by large language models, namely using the chatGPT Application Programming Interface. This R package integrates all steps of the data extraction and validation process, from Optical Character Recognition to detection of outliers and output in tabular format. Furthermore, we validate ARETE through systematic comparison between what is modelled and the work of human annotators. 3. We demonstrate the usefulness of the approach by comparing range maps produced using GBIF data and with those automatically extracted for 100 species of spiders. Newly extracted data allowed to expand the known Extent of Occurrence by a mean three orders of magnitude, revealing new areas where the species were found in the past, which mayhave important implications for spatial conservation planning and extinction risk assessments. 4. ARETE allows faster access to hitherto untapped occurrence data, a potential game changer in projects requiring such data. Researchers will be able to better prioritize resources, manually verifying selected species while maintaining automated extraction for the majority. This workflow also allows predicting available bibliographic data during project planning.
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。
