Simple Local Attentions Remain Competitive for Long-Context Tasks

Understand

Many NLP tasks require processing long contexts beyond the length limit of pretrained models.

• In order to scale these models to longer text sequences, many efficient long-range attention variants have been proposed.
• Despite the abundance of research along this direction, it is still difficult to gauge the relative effectiveness of these models in practical use cases, e.g., if we apply these models following the pretrain-and-finetune paradigm.
• In this work, we aim to conduct a thorough analysis of these emerging models with large-scale and controlled experiments.