TY  - EJOU
AU  - Wu, Jiahao 
AU  - Xu, Jinzhong 
AU  - Liu, Xiaoming 
AU  - Yang, Guan 
AU  - Liu, Jie 

TI  - Causal Representation Enhances Cross-Domain Named Entity Recognition in Large Language Models
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - 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 language models 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.
KW  - Large language model; entity bias; causal graph structure

DO  - 10.32604/cmc.2025.061359