id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-31632	Wang, Zenghua	Analysis on the Method of Improving the Performance of Natural Language Processing Model Driven by Artificial Intelligence	2025	4	.pdf	application/pdf	2409	93	39	As shown in Figure 1, the framework covers data layer, model layer, training layer and deployment layer: in the data layer, low-resource data enhancement and knowledge injection technology are adopted to reduce the dependence on large-scale annotation data [3]; Design a dynamic sparse compression architecture in the model layer, taking into account the model accuracy and computational efficiency; Adaptive multi-task joint learning strategy is introduced in the training layer to enhance the generalization ability of the model in cross-domain and cross-task scenarios [4-5]; Multi-modal semantic alignment reasoning is realized in the deployment layer, which supports efficient and accurate interaction in complex real scenes. As shown in Table 1, on FLORES-200, the BLEU reaches 47.5, on X-Cross, the macro F1 reaches 84.7, the reasoning delay of edge devices is reduced to 62ms, and the energy consumption is reduced to 2.9J, which verifies the effectiveness and superiority of collaborative optimization among data layer (AKI), model layer (DSGT) and training layer (MAMTL).	cache/fcis-31632.pdf	txt/fcis-31632.txt