Automatic Verbalizer

Automatic verbalizers are crucial components of prompt-based tuning for natural language processing, bridging the gap between a language model's output and predicted class labels. Current research focuses on developing more effective and efficient verbalizer architectures, including mapping-free, label-aware, and prototypical approaches, often employing techniques like evolutionary algorithms, meta-learning, and contrastive learning to optimize their performance. These advancements aim to improve the accuracy and efficiency of few-shot and zero-shot text classification, particularly in multi-class scenarios and low-resource settings, impacting various downstream NLP tasks. The resulting improvements in model performance have significant implications for applications requiring efficient adaptation of large language models to new tasks with limited data.