Showing cs.CLShow all
2 papers · 1 filter
cs.CL2026
Long-Context Generalization with Sparse Attention
Pavlo Vasylenko, Hugo Pitorro, André F. T. Martins +1
Transformer-based architectures traditionally employ softmax to compute attention weights, which produces dense distributions over all tokens in a sequence. While effective in many…
cs.CL2024
How Effective are State Space Models for Machine Translation?
Hugo Pitorro, Pavlo Vasylenko, Marcos Treviso +1
Transformers are the current architecture of choice for NLP, but their attention layers do not scale well to long contexts. Recent works propose to replace attention with linear re…