Token Correlation

Token correlation research investigates how relationships between individual words (tokens) within text or other discrete data influence model performance and learning. Current efforts focus on improving large language models (LLMs) by explicitly modeling these correlations, for example, through techniques that enhance the consistency of pairwise preference data used for training or by leveraging token definitions to create more robust embeddings. Understanding and manipulating token correlations is crucial for advancing natural language processing and other fields, as it directly impacts model accuracy, robustness to adversarial attacks, and the efficiency of training algorithms.