调查古董贩运与生成预训练变压器 (GPT) -3启用知识图:一个案例研究
Shawn Graham1, Donna Yates2, Ahmed El-Roby3
1Department of History, Carleton University, Ottawa, Ontario, Canada.
Open research Europe
|August 30, 2023
概括
我们使用大型语言模型 (GPT-3) 半自动创建古董交易的知识图表,实现与手工方法相匹配的结果,节省了大量时间. 这种方法增强了对考古数据的研究.
科学领域:
- 数字人文学科 数字人文学科
- 考古学的考古学
- 人工智能的人工智能
背景情况:
- 古董贸易产生了大量的非结构化数据,这些数据来自各种来源,如文章,拍卖目录和档案.
- 对这些来源的系统检查对于了解贸易至关重要,但需要大量的劳动力.
- 知识图提供了一个结构化的方式来表示这些数据中的复杂关系.
研究的目的:
- 探索使用大型语言模型 (GPT-3) 半自动化知识图表构建从古董贸易学术文本的有效性.
- 将人工智能生成的知识图的质量和效率与手工创建的知识图进行比较.
- 评估这种方法在发现古董贸易的新见解方面的潜力.
主要方法:
- 使用提示指导的GPT-3从文章中提取主体-预言-对象关系,形成知识图.
- 人工智能生成的知识图与来自同一源材料的手动注释版本进行了比较.
- 知识图被投射到一个神经网络嵌入模型 (Ampligraph) 中,以确定可能的连接和关系.
主要成果:
- 使用GPT-3生成的半自动知识图表产生了与手动创建的知识图表相似的结果.
- 这种人工智能驱动的方法与手动注释相比,可以节省大量的时间.
- 该方法允许分析的材料体积的潜在扩张.
结论:
- 使用大型语言模型半自动创建知识图表是一种可行且有效的方法来分析古董贸易.
- 这种计算方法对处理灰色文献和其他学术格式中发现的考古知识产生了重大影响.
- 该方法提供了一个可扩展的解决方案,用于发现考古学和相关领域的新研究途径.
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