让大模型农业建议更靠谱，精准匹配地区土壤气候条件。
AgriRegion: Region-Aware Retrieval for High-Fidelity Agricultural Advice
- 引入地理元数据与区域优先重排序，避免跨区域错误建议
- 在12个农业子领域测试中，幻觉率降低10%-20%
- 适合需要本地化农业指导的农民和农技推广人员
大型语言模型在信息普惠方面展现出巨大潜力。但在农业领域，通用模型常因上下文幻觉而给出不实建议，或在某些地区科学合理、在另一些地区却造成灾难性后果，这是由于土壤、气候和地方法规差异所致。我们提出AgriRegion，一种专为高保真、区域感知农业咨询设计的检索增强生成（RAG）框架。不同于仅依赖语义相似度的标准RAG方法，AgriRegion引入地理空间元数据注入层和区域优先重排序机制。通过将知识库限制在经验证的本地农业推广服务，并在检索阶段施加地理空间约束，确保种植时间表、病虫害防治和施肥建议具有本地准确性。我们构建了一个新基准数据集AgriRegion-Eval，包含12个农业子领域的160个专业问题。实验表明，相比最先进语言模型系统，AgriRegion将幻觉率降低了10%-20%，且综合评估显示信任度显著提升。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated significant potential in democratizing access to information. However, in the domain of agriculture, general-purpose models frequently suffer from contextual hallucination, which provides non-factual advice or answers are scientifically sound in one region but disastrous in another due to variations in soil, climate, and local regulations. We introduce AgriRegion, a Retrieval-Augmented Generation (RAG) framework designed specifically for high-fidelity, region-aware agricultural advisory. Unlike standard RAG approaches that rely solely on semantic similarity, AgriRegion incorporates a geospatial metadata injection layer and a region-prioritized re-ranking mechanism. By restricting the knowledge base to verified local agricultural extension services and enforcing geo-spatial constraints during retrieval, AgriRegion ensures that the advice regarding planting schedules, pest control, and fertilization is locally accurate. We create a novel benchmark dataset, AgriRegion-Eval, which comprises 160 domain-specific questions across 12 agricultural subfields. Experiments demonstrate that AgriRegion reduces hallucinations by 10-20% compared to state-of-the-art LLMs systems and significantly improves trust scores according to a comprehensive evaluation.
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