Absolute Position

Absolute position embeddings (APEs) are crucial for encoding word order in transformer-based language models, but their reliance on absolute rather than relative position is a growing concern. Current research focuses on improving APEs' effectiveness, exploring alternative methods like relative positional embeddings and developing techniques to extend the context length handled by models using APEs, including modifications to attention mechanisms and training procedures. These efforts aim to enhance the performance and efficiency of language models, particularly in handling long sequences, and to better understand how these models process sequential information.