基于图增强推理的烟草害虫和疾病管理
Graph-Augmented Reasoning with Large Language Models for Tobacco Pest and Disease Management
AI总结：
本文提出一种基于图增强推理的框架，通过整合领域知识图谱，提升烟草害虫和疾病管理的推理能力和推荐准确性。
摘要：
本文提出了一种用于烟草害虫和疾病管理的图增强推理框架，该框架将结构化的领域知识整合到大型语言模型中。在GraphRAG的基础上，我们构建了一个领域特定的知识图谱，并检索与查询相关的子图，以在回答生成过程中提供关系证据。该框架采用ChatGLM作为Transformer的主干模型，并采用基于LoRA的参数高效微调方法，同时利用图神经网络学习节点表示，以捕捉症状-疾病-治疗之间的依赖关系。通过显式地将疾病、症状、杀虫剂和控制措施建模为相互关联的实体，系统支持超越表面文本相似度的证据感知检索。检索到的图证据被整合到LLM输入中，以引导生成向领域一致的推荐，并减少幻觉或不恰当的治疗。实验结果表明，与纯文本基线相比，该框架在多跳和比较推理问题上表现出了持续的改进，其中在需要链接多个关系的问题上获得了最大的提升。
英文摘要：
This paper proposes a graph-augmented reasoning framework for tobacco pest and disease management that integrates structured domain knowledge into large language models. Building on GraphRAG, we construct a domain-specific knowledge graph and retrieve query-relevant subgraphs to provide relational evidence during answer generation. The framework adopts ChatGLM as the Transformer backbone with LoRA-based parameter-efficient fine-tuning, and employs a graph neural network to learn node representations that capture symptom-disease-treatment dependencies. By explicitly modeling diseases, symptoms, pesticides, and control measures as linked entities, the system supports evidence-aware retrieval beyond surface-level text similarity. Retrieved graph evidence is incorporated into the LLM input to guide generation toward domain-consistent recommendations and to mitigate hallucinated or inappropriate treatments. Experimental results show consistent improvements over text-only baselines, with the largest gains observed on multi-hop and comparative reasoning questions that require chaining multiple relations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
