LitAI:通过生成人工智能增强多模式文献理解和采矿
Gowtham Medisetti1, Zacchaeus Compson1, Heng Fan1
1University of North Texas, Denton, TX, USA.
概括
通过使用生成性AI和光学字符识别 (OCR) 来从PDF中提取文本,表格和图形,LitAI提高了科学文献检索. 这种方法优于生物和生态科学中的现有方法.
科学领域:
- 信息检索 信息检索
- 科学文献分析,科学文献分析.
- 在研究中的人工智能.
背景情况:
- 科学研究依赖于有效的信息处理和从各种文献格式中检索信息.
- 多模式文献 (文本,表格,图形) 对传统的信息检索系统构成了挑战.
研究的目的:
- 引入LitAI,一种用于从科学文献中增强多式联络信息检索的新方法.
- 利用生成性AI和光学字符识别 (OCR) 来从PDF文档中提取信息.
主要方法:
- 开发了LitAI,将OCR与生成AI服务集成在一起,用于多式联网数据提取.
- 在生成AI中利用快速工程和上下文学习来精确提取信息.
- 在生态和生物科学数据集上评估了LitAI.
主要成果:
- 与基线相比,LitAI在检索文本,表格和图表方面表现出色.
- 经验评估显示,与Tesseract-OCR和GPT-4相比,有显著的改善.
- 该方法有效地处理科学PDF中的多式联络信息.
结论:
- 在科学文献中,LitAI提供了一种强大且易于使用的多式联络信息检索解决方案.
- 生成型人工智能与OCR相结合,显著提高了文献分析工具的功能.
- 开发的提示和方法为人工智能驱动的科学发现的未来研究提供了坚实的框架.
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