EncAttAgg introduced two attenders to tackle two problems: 1) We introduce a mutual attender layer to efficiently obtain the entity-pair-specific mention representations. 2) We introduce an integration attender to weight mention pairs of a target entity pair.

• Parallel Layers – We use a “parallel” formulation in each Transformer block (Wang & Komatsuzaki, 2021), rather than the standard “serialized” formulation. Specifically, the standard formulation can be written as:   
    y = x + MLP(LayerNorm(x + Attention(LayerNorm(x)))   

Whereas the parallel formulation can be written as:   
    y = x + MLP(LayerNorm(x)) + Attention(LayerNorm(x))   

The parallel formulation results in roughly 15% faster training speed at large scales, since the MLP and Attention input matrix multiplications can be fused. Ablation experiments showed a small quality degradation at 8B scale but no quality degradation at 62B scale, so we extrapolated that the effect of parallel layers should be quality neutral at the 540B scale.

Parallel Layers

PaLM: Scaling Language Modeling with Pathways

EncAttAgg

Compress an ensemble of models into a single one by averaging their weights (under certain pre-conditions).

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com