Easy to Learn Token

Research on "easy-to-learn tokens" focuses on addressing the imbalance in the learning process of language models, where frequent tokens are over-represented while infrequent ones are under-represented. Current efforts utilize transformer architectures and novel loss functions, such as those incorporating information entropy to dynamically weight training based on token difficulty, to improve model performance and efficiency by mitigating this bias. This work is significant because it enhances the robustness and generalization capabilities of large language models, leading to improved performance on downstream tasks and a deeper understanding of how these models process information.