测试基于人工智能的大型语言模型的可靠性,以从科学文献中提取生态信息
Andrew V Gougherty1, Hannah L Clipp2
1USDA Forest Service Northern Research Station, Delaware, OH, USA. andrew.gougherty@usda.gov.
npj biodiversity
|September 6, 2024
概括
大型语言模型 (LLM) 可以比人类提取50倍以上的生态数据,对离散数据具有很高的准确性. 然而,量化数据的提取需要对可靠的生态数据库进行进一步的质量保证.
科学领域:
- 生态研究生态研究
- 数据科学是数据科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 大型语言模型 (LLM) 显示了促进生态研究效率和规模的前景.
- 有关LLM准确性和科学应用中错误信息的可能性存在担忧.
研究的目的:
- 评估与人类审稿人相比,LLMs从科学文献中提取生态数据的速度和准确性.
- 为了识别生态数据的类型,LLM优秀或扎.
主要方法:
- 在LLM和执行数据提取任务的人类审核员之间进行了正式比较.
- 从科学文献中提取生态数据是主要任务.
- 性能指标包括不同数据类型的提取速度和准确性.
主要成果:
- 该LLM提取的数据超过50倍比人类审查员更快.
- 对于离散和分类生态数据,LLM的准确性超过了90%.
- 在提取某些类型的定量生态数据方面,LLM表现不佳.
结论:
- 在快速生成大规模生态数据库方面,LLM具有显著的潜力.
- 目前在生态数据提取中的LLM应用需要增强的质量保证协议来保证数据完整性.
- 需要进一步开发,以提高在生态学中提取定量数据的LLM准确性.
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