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

Zhao Weikang; Wang Haoyu; Hua Jing; Liu Yaobing; Wang Xiujuan; Kang Mengzhen

doi:10.11959/j.issn.2096-6652.202603

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

Papers and Reports | 更新时间：2026-04-27

- LinYun: a domain-specific large language model for intelligent question answering and decision support in forestry
- Chinese Journal of Intelligent Science and Technology Vol. 8, Issue 1, Pages: 83-93(2026)
- 作者机构：
  
  1.中国科学院自动化研究所多模态人工智能系统全国重点实验室，北京 100190
  2.中国科学院大学人工智能学院，北京 100049
  3.北京江恒数智生态科技有限公司，北京 102628
  4.澳门科技大学创新工程学院，澳门 999078
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62076239)
- DOI：10.11959/j.issn.2096-6652.202603
  CLC： TP391.4
- Received：30 September 2025，
  
  Revised：2026-02-02，
  
  Accepted：12 March 2026，
  
  Published：15 March 2026
- 稿件说明：
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赵维康,王浩宇,华净等.LinYun：林业领域智能问答与决策支持的大型语言模型研究[J].智能科学与技术学报,2026,08(01):83-93.

Zhao Weikang,Wang Haoyu,Hua Jing,et al.LinYun: a domain-specific large language model for intelligent question answering and decision support in forestry[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):83-93.
赵维康,王浩宇,华净等.LinYun：林业领域智能问答与决策支持的大型语言模型研究[J].智能科学与技术学报,2026,08(01):83-93. DOI： 10.11959/j.issn.2096-6652.202603.

Zhao Weikang,Wang Haoyu,Hua Jing,et al.LinYun: a domain-specific large language model for intelligent question answering and decision support in forestry[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):83-93. DOI： 10.11959/j.issn.2096-6652.202603.

摘要

林业知识具有高度的专业性，涵盖范围广泛，并对法规及实践标准高度敏感。针对目前通用大语言模型在林业场景中存在的知识缺口、术语歧义以及事实性错误等问题，提出“数据合成-模型训练-系统化评测”一体化框架，基于通用基座模型进行领域指令微调，得到面向林业领域适配的模型林云（LinYun）。实验结果表明，LinYun在林业相关任务上显著优于同规模的通用模型，在部分任务上接近甚至超过更大规模模型的表现。

Abstract

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.

关键词

Keywords

references

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