Graph-Augmented Reasoning with Large Language Models for Tobacco Pest and Disease Management

• URL: http://arxiv.org/abs/2602.02635v1
• Date: Mon, 02 Feb 2026 18:29:52 GMT
• Title: Graph-Augmented Reasoning with Large Language Models for Tobacco Pest and Disease Management
• Authors: Siyu Li, Chenwei Song, Qi Zhou, Wan Zhou, Xinyi Liu,
• Abstract summary: This paper proposes a graph-augmented reasoning framework for tobacco pest and disease management.<n>We construct a domain-specific knowledge graph and retrieve query-relevant subgraphs to provide relational evidence during answer generation.
• Score: 9.759725097042656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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