General Knowledge

Longformer is a modified Transformer architecture. Traditional Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this, Longformer uses an attention pattern that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. The attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. The attention patterns utilised include: sliding window attention, dilated sliding window attention and global + sliding window. These can be viewed in the components section of this page.

RealFormer is a type of Transformer based on the idea of residual attention. It adds skip edges to the backbone Transformer to create multiple direct paths, one for each type of attention module. It adds no parameters or hyper-parameters. Specifically, RealFormer uses a Post-LN style Transformer as backbone and adds skip edges to connect Multi-Head Attention modules in adjacent layers.

SimAdapter is a module for explicitly learning knowledge from adapters. SimAdapter aims to learn the similarities between the source and target languages during fine-tuning using the adapters, and the similarity is based on an attention mechanism. The detailed composition of the SimAdapter is shown in the Figure. By taking the language-agnostic representations from the backbone model as the query, and the language-specific outputs from multiple adapter as the keys and values, the final output for SimAdapter over attention are computed as (For notation simplicity, we omit the layer index $l$ below): $$ \operatorname{SimAdapter}\left(\mathbf{z}, \mathbf{a}\_{\left\(S\_{1}, S\_{2}, \ldots, S\_{N}\right\)}\right)=\sum_{i=1}^{N} \operatorname{Attn}\left(\mathbf{z}, \mathbf{a}\_{S\_{i}}\right) \cdot\left(\mathbf{a}\_{S\_{i}} \mathbf{W}\_{V}\right) $$ where SimAdapter $(\cdot)$ and $\operatorname{Attn}(\cdot)$ denotes the SimAdapter and attention operations, respectively. Specifically, the attention operation is computed as: $$ \operatorname{Attn}(\mathbf{z}, \mathbf{a})=\operatorname{Softmax}\left(\frac{\left(\mathbf{z} \mathbf{W}\_{Q}\right)\left(\mathbf{a} \mathbf{W}\_{K}\right)^{\top}}{\tau}\right) $$ where $\tau$ is the temperature coefficient, $\mathbf{W}\_{Q}, \mathbf{W}\_{K}, \mathbf{W}\_{V}$ are attention matrices. Note that while $\mathbf{W}\_{Q}, \mathbf{W}\_{K}$ are initialized randomly, $\mathbf{W}\_{V}$ is initialized with a diagonal of ones and the rest of the matrix with small weights $(1 e-6)$ to retain the adapter representations. Furthermore, a regularization term is introduced to avoid drastic feature changes: $$ \mathcal{L}\_{\mathrm{reg}}=\sum\_{i, j}\left(\left(\mathbf{I}\_{V}\right)\_{i, j}-\left(\mathbf{W}\_{V}\right)_{i, j}\right)^{2} $$ where $\mathbf{I}\_{V}$ is the identity matrix with the same size as $\mathbf{W}\_{V}$