大型语言模型和农业推广服务.
A Tzachor1,2, M Devare3, C Richards4,5
1CSER, University of Cambridge, Cambridge, UK. atzachor@runi.ac.il.
Nature food
|November 6, 2023
概括
大型语言模型 (LLM) 通过简化科学知识,显示出农业扩展的前景. 现实世界的测试揭示了局限性,强调了为安全的全球部署需要专家指导的开发.
科学领域:
- 农业科学 农业科学
- 人工智能的人工智能
- 信息技术 信息技术 信息技术
背景情况:
- 传统的农业推广服务面临着有限的能力和范围的挑战.
- 需要创新的解决方案来改善农业知识传播.
研究的目的:
- 评估大型语言模型 (LLM) 的潜力,例如生成预训练变压器 (GPT),用于农业扩展.
- 评估LLM简化科学知识和提供量身定制的农业建议的能力.
- 通过现实世界的测试来确定在农业环境中LLMs的局限性.
主要方法:
- 专注于用于农业扩展的发电预训练变压器 (GPT) 的应用.
- 进行了GPT的现实生活测试,为尼日利亚的麻豆种植者提供技术建议.
- 分析了LLM在提供农业建议方面的能力和缺点.
主要成果:
- 法律法规可以为农民简化复杂的科学信息.
- 个性化,特定于位置和数据驱动的建议是潜在的好处.
- 测试揭示了LLM产生的农业建议的实际局限性和改进的需要.
结论:
- 在全球范围内,LLM为农业推广服务提供了变革性的潜力.
- 人在循环中的方法对于设计和部署LLM在农业中的责任至关重要.
- 需要进一步开发,以克服目前的技术缺陷,以便广泛采用.
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