Token Level

Token-level analysis in large language models (LLMs) focuses on understanding the individual units of text and their contribution to overall model behavior and performance. Current research investigates token dynamics within various architectures, including transformers and state space models, exploring techniques like token caching, selective training, and retrieval augmentation to improve efficiency and accuracy. This granular approach is crucial for enhancing LLM capabilities in diverse applications, from improving machine translation and gene expression prediction to mitigating biases and enhancing robustness against attacks. The insights gained are driving advancements in model training, optimization, and interpretability.