Verbalizer Manipulation

Verbalizer manipulation in prompt-based learning focuses on improving the mapping between model predictions and output labels, a crucial step in various natural language processing tasks like text classification and information extraction. Current research explores methods for automatically generating or refining verbalizers, including techniques that leverage scenario-specific concepts and mapping-free architectures, aiming to enhance model robustness and performance, particularly in multi-class scenarios. This research is significant because effective verbalizers are key to unlocking the full potential of prompt-based models, leading to more accurate and reliable results across diverse applications.