Navigating the Gen-AI Frontier : Transformers, GPT and the path to Accelerated Innovation

Introduction to Generative Artificial Intelligence (GenAI) :

Generative artificial intelligence (GenAI), exemplified by ChatGPT, Midjourney, and other state-of-the-art large language models and diffusion models, holds significant potential for transforming education and enhancing human productivity. While the prevalence of GenAI in education has motivated numerous research initiatives, integrating these technologies within the learning analytics (LA) cycle and their implications for practical interventions remain underexplored.

1. Historical Context: Seq2Seq Paper and NMT by Joint Learning to Align & Translate Paper

Sequence to Sequence :

The "Sequence to Sequence Learning with Neural Networks," published in 2014 by Ilya Sutskever, Oriol Vinyals, and Quoc V. Le, introduced a groundbreaking approach to sequence-to-sequence learning. This model, based on Recurrent Neural Networks (RNNs), laid the foundation for various sequence generation tasks, notably machine translation.

Sequence To Sequence, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one LSTM, the encoder, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a context vector), and then to use another LSTM, the decoder, to extract the output sequence from that vector. The second LSTM is essentially a recurrent neural network language model except that it is conditioned on the input sequence.

Neural Machine Translation (NMT) :

Neural machine translation is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder-decoders and consists of an encoder that encodes a source sentence into a fixed-length vector from which a decoder generates a translation.

2. Introduction to Transformers (Paper: Attention is all you need)

The introduction of the Transformer model through the paper "Attention is All You Need" represents a pivotal moment in the field of natural language processing (NLP) and deep learning. Published in 2017 by Vaswani et al., this paper proposed a novel architecture for sequence-to-sequence learning without recurrent neural networks (RNNs) or convolutional neural networks (CNNs).

Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.

3. Why Transformers?

Introduced by Vaswani et al. in "Attention is All You Need"

A Transformer is a model architecture that eschews recurrence and instead relies entirely on an attention mechanism to draw global dependencies between input and output. Before Transformers, the dominant sequence transduction models were based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The Transformer also employs an encoder and decoder, but removing recurrence in favor of allows for significantly more parallelization than methods like RNN's and CNN's.

4.Explain the working of each transformer component.

• Input Embeddings: Represent input tokens as vectors.
• Positional Encodings: Add positional information to input embeddings.
• Multi-Head Self-Attention: Weigh token importance based on context.
• Position-wise Feedforward Networks: Apply non-linear transformations to attention output.
• Layer Normalization and Residual Connections: Stabilize training and facilitate gradient flow.
• Output Layer: Produce probability distribution over output vocabulary for predictions.

5. How is GPT-1 trained from Scratch?

Introduced by Radford et al. in Improving Language Understanding by Generative Pre-Training . GPT is a Transformer-based architecture and training procedure for natural language processing tasks. Training follows a two-stage procedure. First, a language modeling objective is used on the unlabeled data to learn the initial parameters of a neural network model. Subsequently, these parameters are adapted to a target task using the corresponding supervised objective.