7 papers · 1 filter
DashAttention: Differentiable and Adaptive Sparse Hierarchical Attention
Yuxiang Huang, Nuno M. T. Gonçalves, Federico Alvetreti +5
Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-k relevant key-value (KV) blocks based on coarse attention scores and subsequently apply fine-grain…
AMALIA Technical Report: A Fully Open Source Large Language Model for European Portuguese
Afonso SimplÃcio, Gonçalo Vinagre, Miguel Moura Ramos +19
Despite rapid progress in open large language models (LLMs), European Portuguese (pt-PT) remains underrepresented in both training data and native evaluation, with machine-translat…
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…
AdaSplash: Adaptive Sparse Flash Attention
Nuno Gonçalves, Marcos Treviso, André F. T. Martins
The computational cost of softmax-based attention in transformers limits their applicability to long-context tasks. Adaptive sparsity, of which -entmax attention is an example,…
LaTIM: Measuring Latent Token-to-Token Interactions in Mamba Models
Hugo Pitorro, Marcos Treviso
State space models (SSMs), such as Mamba, have emerged as an efficient alternative to transformers for long-context sequence modeling. However, despite their growing adoption, SSMs…
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…