Fine Grained Token

Fine-grained tokenization in language models focuses on leveraging individual tokens (words or sub-word units) to improve model performance and understanding. Current research emphasizes refining token-level calibration for more accurate uncertainty quantification and using fine-grained token-level supervision to enhance model alignment and performance in tasks like classification and knowledge graph completion. This granular approach addresses limitations of coarser methods by enabling more precise control over model behavior and leading to improvements in various applications, including multi-modal learning and interactive action recognition. The resulting advancements contribute to more robust, reliable, and efficient language models.