[Purpose/Significance] The development of large-scale pre-trained models has opened a new stage for artificial intelligence. More and more researchers have adopted models such as BERT and GPT in various application fields. However, the problems of complexity and interpretability have posed a degree of risk to the model’s generalization. Meanwhile, the manual annotation cost of training data in classification tasks is high, and its quality directly affects the usability of the model. Automated optimization of data annotation quality often leads to resource savings and enhances the training efficiency and usability of models. [Method/Process] This article proposed the GSBERT model. The model used gradient saliency to explore the decision-making basis of the BERT and presented the BERT model decision-making process through visualization, thereby making model decisions interpretable. Based on the calculation of feature words through gradient saliency, the distribution of sample features was extracted as the basis for GSBERT to detect annotations. To test the effectiveness of GSBERT in analyzing data consistency, the model was applied to re-annotate the “misclassified” samples from the news classification datasets. The improved annotation quality led to a 12.55% increase in accuracy on the validation set. [Result/Conclusion] This study explores a novel and effective method for mining the value of data from the perspective of large model interpretability, aiming to provide insights for broader applications of deep learning techniques across various fields.
Jiang Lei
,
Zhang Tao
,
Ma Haiqun
,
Li Zheng
. GSBERT: An Empirical Study of an Automatic Annotation Detection Method for Datasets Based on Visual Interpretation[J]. Library and Information Service, 2025
, 69(11)
: 111
-122
.
DOI: 10.13266/j.issn.0252-3116.2025.11.010
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