Multiple Choice Question Answering (MCQA)

Galactica is a language model which uses a Transformer architecture in a decoder-only setup with the following modifications: - It uses GeLU activations on all model sizes - It uses a 2048 length context window for all model sizes - It does not use biases in any of the dense kernels or layer norms - It uses learned positional embeddings for the model - A vocabulary of 50k tokens was constructed using BPE. The vocabulary was generated from a randomly selected 2% subset of the training data

• 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.