基于大型语言模型的农业问答系统的开发和评估
1Department of Computer Engineering, Karamanoğlu Mehmetbey University, Karaman, 70100, Turkey. ayseeldem@kmu.edu.tr.
Scientific reports
|February 9, 2026
概括
大型语言模型 (LLM) 显示了农业的潜力,但性能因模型和提示策略而异. 本研究评估了农业问题答案的LLM,为智能数字应用提供了洞察力.
科学领域:
- 农业科学 农业科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在各种领域展示了先进的功能.
- 目前,LLM在农业中的应用仍然有限且未得到充分发展.
- 需要对农业特定任务的LLM绩效进行全面评估.
研究的目的:
- 在农业领域对大型语言模型 (LLM) 进行全面评估.
- 评估不同提示策略 (零射击,CoT,自我一致性,ToT) 和自动提示工程 (APE) 的有效性,以回答农业问题.
- 分析即时方法对LLM生成答案的准确性和一致性的影响.
主要方法:
- 开发一套多选题,涵盖一般,园艺和作物生产主题,在简单,中等和困难级别.
- 使用GPT-4o和Gemini-2.0闪光的LLM与各种提示策略生成答案.
- 使用自动提示工程 (APE) 应用自动优化的提示管道.
- 统计分析结果使用启动过程的置信区间,配对的t测试,ANOVA和效果大小测量.
主要成果:
- 在农业问题回答方面,LLM 取得了普遍的成功,其表现因LLM 和采用的快速策略而有很大差异.
- 快速策略和LLM选择影响了生成答案的准确性和一致性.
- 统计分析证实了在LLM,快速方法,难度级别和类别之间表现的显著差异.
结论:
- 在农业应用中,LLM具有显著的前景,特别是在问答系统中.
- 优化提示策略对于提高LLM在农业等专业领域的性能和可靠性至关重要.
- 这项研究为开发针对专家的农业智能数字应用程序奠定了基础.
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