Transformer Length Extrapolation

Transformer length extrapolation focuses on enabling transformer models to process sequences significantly longer than those seen during training, improving their applicability to long-range dependencies in various data types. Current research emphasizes improving positional embeddings and attention mechanisms, exploring techniques like relative positional encodings, attention alignment strategies, and incorporating convolutional operations on attention scores to enhance model expressiveness and extrapolation capabilities. This research is significant because it addresses a key limitation of standard transformers, paving the way for improved performance in tasks involving long sequences, such as long-term time series forecasting and processing extensive text corpora.