ABSTRACT Prompt tuning‐based few‐shot text classification aims to improve model performance by constructing high‐quality verbalizer. However, existing methods suffer from high subjective bias, insufficient semantic coverage, and uneven representation ability of label words, which limits the further improvements in classification performance. To address these challenges, we propose KPTUltra. The model synergistically integrates multiple pre‐trained models and contrastive learning through class‐sensitive ranking method (CSR) to construct a robust semantic embedding space. Additionally, a genetic algorithm is employed to optimise the mapping between label word and class, enhancing screening stability and semantic matching. Secondly, we introduce a genetic algorithm‐based adaptive label word weight optimization mechanism (GAAWO), which dynamically adjusts both the composition and the weight distribution of label words in the latent space. This enables fine‐grained control and effectively reduces the impact of low‐representative label words. Extensive experiments on multiple few‐shot text classification benchmarks demonstrate that KPTUltra outperforms state‐of‐the‐art baseline methods, achieving superior overall performance.
Zha et al. (2026) studied this question.