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SaulLM-7B: A pioneering Large Language Model for Law
In this paper, we introduce SaulLM-7B, a large language model (LLM) tailored for the legal domain.
One Wide Feedforward is All You Need
The Transformer architecture has two main non-embedding components: Attention and the Feed Forward Network (FFN).
Learning Language-Specific Layers for Multilingual Machine Translation
Multilingual Machine Translation promises to improve translation quality between non-English languages.
State Spaces Aren't Enough: Machine Translation Needs Attention
Structured State Spaces for Sequences (S4) is a recently proposed sequence model with successful applications in various tasks, e. g. vision, language modeling, and audio.
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