Long Input Transformer

Long-input Transformers aim to overcome the limitations of standard Transformer models in processing lengthy text sequences, a crucial challenge for many natural language processing tasks. Current research focuses on developing more efficient architectures, such as those employing recursive processing, conditional computation prioritizing important tokens, and optimized attention mechanisms to reduce computational complexity while maintaining accuracy. These advancements enable improved performance on tasks involving long documents, like question answering and summarization, and are particularly beneficial for multilingual applications and low-resource languages where large datasets of long texts are scarce. The resulting models offer significant improvements in speed and memory efficiency compared to their predecessors.