Adversarial Robustness in Text Classification through Semantic Calibration with Large Language Models

This paper addresses the problem of text classification models being vulnerable and lacking robustness under adversarial perturbations by proposing a robust text classification method based on large language model calibration. The method builds on a pretrained language model and constructs a multi-stage framework for semantic representation and confidence regulation. It achieves stable optimization of classification results through semantic embedding extraction, calibration adjustment, and consistency constraints. First, the model uses a pretrained encoder to generate context-aware semantic features and applies an attention aggregation mechanism to obtain global semantic representations. Second, a temperature…