Multi-task Language Understanding

GPT-4 is a transformer based model pre-trained to predict the next token in a document.

LLaMA is a collection of foundation language models ranging from 7B to 65B parameters. It is based on the transformer architecture with various improvements that were subsequently proposed. The main difference with the original architecture are listed below. - RMSNorm normalizing function is used to improve the training stability, by normalizing the input of each transformer sub-layer, instead of normalizing the output. - The ReLU non-linearity is replaced by the SwiGLU activation function to improve performance. - Absolute positional embeddings are removed and instead rotary positional embeddings (RoPE) are added at each layer of the network.

Flan-T5 is the instruction fine-tuned version of T5 or Text-to-Text Transfer Transformer Language Model.

GLM is a bilingual (English and Chinese) pre-trained transformer-based language model that follow the traditional architecture of decoder-only autoregressive language modeling. It leverages autoregressive blank infilling as its training objective.

GPT-NeoX is an autoregressive transformer decoder model whose architecture largely follows that of GPT-3, with a few notable deviations. The model has 20 billion parameters with 44 layers, a hidden dimension size of 6144, and 64 heads. The main difference with GPT-3 is the change in tokenizer, the addition of Rotary Positional Embeddings, the parallel computation of attention and feed-forward layers, and a different initialization scheme and hyperparameters.

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

UL2 is a unified framework for pretraining models that are universally effective across datasets and setups. UL2 uses Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together. UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes.

• 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.