LinYun: a domain-specific large language model for intelligent question answering and decision support in forestry

Forestry knowledge is highly specialized and broad in scopeand is particularly sensitive to regulations and practical standards. To address the knowledge gaps, terminological ambiguities, and factual inaccuracies that general-purpose large language model (LLM) exhibit in forestry scenarios, an integrated framework of "data synthesis-model training-systematic evaluation" was proposed. Based on a general base model, domain-specific instruction fine-tuning was conducted to obtain LinYun, a domain-adapted model for forestry. Experimental results demonstrate that LinYun significantly outperforms general-purpose models of comparable scale in forestry-related tasks, and in some tasks approaches or even surpasses the performance of much larger models.