Token Alignment

Token alignment in machine learning focuses on improving model performance by ensuring consistent and meaningful relationships between input tokens (e.g., words, image patches, genetic sequences) and their corresponding labels or representations. Current research emphasizes aligning tokens across different modalities (e.g., text and images, genes and language), hierarchical structures (e.g., in text classification), and domains (e.g., in cross-domain named entity recognition), often leveraging large language models and contrastive learning techniques. These advancements are improving the accuracy and interpretability of models in diverse applications, ranging from gene expression prediction and medical code classification to image-text understanding and text generation. The ultimate goal is to create more robust and reliable models by addressing issues like token uniformity and label conflicts.