Causal Representation Enhances Cross-Domain Named Entity Recognition in Large Language Models  

作　　者：Jiahao Wu Jinzhong Xu Xiaoming Liu Guan Yang Jie Liu 

机构地区：[1]School of Artificial Intelligence,Zhongyuan University of Technology,Zhengzhou,450007,China [2]School of Computer Science,Zhongyuan University of Technology,Zhengzhou,450007,China [3]Zhengzhou Key Laboratory of Text Processing and Image Understanding,Zhengzhou,450007,China [4]School of Information Science and Technology,North China University of Technology,Beijing,100144,China

出　　处：《Computers, Materials & Continua》2025年第5期2809-2828,共20页计算机、材料和连续体(英文)

基　　金：supported by National Natural Science Foundation of China Joint Fund for Enterprise Innovation Development(U23B2029);National Natural Science Foundation of China(62076167,61772020);Key Scientific Research Project of Higher Education Institutions in Henan Province(24A520058,24A520060,23A520022);Postgraduate Education Reform and Quality Improvement Project of Henan Province(YJS2024AL053).

摘　　要：Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information between different domains,which makes large language models prone to spurious correlations problems when dealing with specific domains and entities.In order to solve this problem,this paper proposes a cross-domain named entity recognition method based on causal graph structure enhancement,which captures the cross-domain invariant causal structural representations between feature representations of text sequences and annotation sequences by establishing a causal learning and intervention module,so as to improve the utilization of causal structural features by the large languagemodels in the target domains,and thus effectively alleviate the false entity bias triggered by the false relevance problem;meanwhile,through the semantic feature fusion module,the semantic information of the source and target domains is effectively combined.The results show an improvement of 2.47%and 4.12%in the political and medical domains,respectively,compared with the benchmark model,and an excellent performance in small-sample scenarios,which proves the effectiveness of causal graph structural enhancement in improving the accuracy of cross-domain entity recognition and reducing false correlations.

关 键 词：Large language model entity bias causal graph structure 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]