中圖分類號：TP391.4文獻標志碼：ADOI:10.19358/j.issn.2097-1788.2026.07.005中文引用格式：黃起豹,譚先華. 生成式語言模型的知識嵌入與層級認知推理框架研究［J］.網絡安全與數據治理，2026，45（7）：32-38. 英文引用格式：Huang Qibao，Tan Xianhua. Research on knowledge embedding and hierarchical cognitive reasoning framework of generative language model［J］.Cyber Security and Data Governance，2026，45（7）：32-38.
Research on knowledge embedding and hierarchical cognitive reasoning framework of generative language model
Huang Qibao1，Tan Xianhua2
1. College of Digital Technology Application Industries，Shangrao Normal University； 2. School of Law and Business，Shangrao Normal University
Abstract： Existing cognitive theories of Generative Language Models (GLMs) are primarily based on statistical connectionism, suffering from three major limitations: implicit knowledge representation, superficial cognitive processing, and disconnection between data and knowledge. From an interdisciplinary perspective of cognitive science and linguistics, this paper proposes a three-in-one cognitive theory reconstruction framework consisting of "knowledge embedding, hierarchical reasoning, and dynamic fusion". The framework constructs a multi-dimensional linguistic knowledge ontology covering syntax, semantics, pragmatics, and cognitive schemas, designs a three-tier cognitive reasoning architecture of "syntactic parsingsemantic inferencepragmatic adaptation", and introduces an attentiondriven adaptive dataknowledge fusion model. Multi-model comparison experiments, and generalization tests based on the CoG-LM-2025 evaluation dataset demonstrate that the reconstructed framework significantly improves model performance in high-order tasks such as deep semantic inference, complex spatial reasoning, and implicit pragmatic meaning extraction. The cognitive interpretability score increases by 2.6 points compared to baseline models, while the average generallization attenuation rate has dropped to 8.3%. The study confirms that systematic optimization of cognitive architecture provides a viable approach to enhance the structured reasoning and interpretability of generative models.
Key words : generative language models; cognitive theory; knowledge ontology; hierarchical reasoning; interpretability
引言
GPT-4、Claude3、Qwen-2等生成式語言模型(Generative Language Models,GLMs)依托大規模預訓練與自監督學習，在文本生成、翻譯等表層自然語言處理(Natural Language Processing,NLP)任務中性能逼近甚至超越人類，推動NLP邁入“數據驅動”的規模化發展階段［1］。這類模型的核心優勢在于無需人工編碼語言學規則，通過學習語料統計特征構建語義表征，實現語言能力的泛化。但在深層語義理解、復雜邏輯推理等高階任務中，其認知局限性日益凸顯，語義偏差、常識錯誤等問題頻發［2］。以GPT4為例，其簡單方位判斷準確率可達92%，但面對雙向認知推理表達時，準確率卻驟降至38.7%。其根源就是模型僅能捕捉表層語言特征，缺乏人類式的深層認知邏輯。
