A math word problem is a mathematical exercise (such as in a textbook, worksheet, or exam) where significant background information on the problem is presented in ordinary language rather than in mathematical notation. As most word problems involve a narrative of some sort, they are sometimes referred to as story problems and may vary in the amount of technical language used.
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.
DeBERTa is a Transformer-based neural language model that aims to improve the BERT and RoBERTa models with two techniques: a disentangled attention mechanism and an enhanced mask decoder. The disentangled attention mechanism is where each word is represented unchanged using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangle matrices on their contents and relative positions. The enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens for model pre-training. In addition, a new virtual adversarial training method is used for fine-tuning to improve model’s generalization on downstream tasks.
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