Navigating the GenAI Frontier :Transformers ,GPT and the Path to Accelerated Innovation

 

Generative AI 👇:

      Let’s start with the concept of Generative AI. This is more than a fashionable buzzword. It is a form of artificial intelligence in which algorithms generate data instead of just analyzing it. GenAI makes it possible to create new content, ranging from text to images, videos, speech and even music. It can thus achieve realistic and personal interactions between humans and machines.

Several specific examples of Generative AI is used for:

• Question Answer Systems
• Summarizing the text
• Language Translation
• Chatbot

Let's Explore in detail :

   ✓ Historical Context

   ✓ Introduction to Transformers

   ✓ Why Transformers

   ✓ Explaining Transformer's Components

   ✓ How GPT-1 is Trained from Scratch

Historical Context:

Before 2013 era , Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. Then Google come up with new idea  to resolve the sequence to sequence mapping.

Paper :Sequence to Sequence Learning with Neural Networks

 ✔ This paper came in 2014 , this paper  is about to use Sequence to Sequence technique for preserving the sequence meaning capture. 

✔ This method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Experimenting to translate the language English to French task on WMT'14 dataset, this task got test score as 34.8. 

✔ This result found that reversing the order of the words in all source sentences (but not target sentences) improved the LSTM’s performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.

✔ With this concept introduced Encoder and Decoder 

Drawbacks of Seq2seq paper:

• fixed-length context vector and Long term Dependence

            By introducing sequence to sequence technique and powerful LSTM architectures it used to rely on generating a single fixed-length context for the entire input sequence , which can lead to long term dependence (information loss) ,especially for longer sequences and passing single token at a time it'll take lot of time to train.

Paper :Neural Machine Translation by Joint Learning to Align and Translate

✔ This paper came in 2015 by introducing Attention mechanism to solve the information loss issue on huge data.

✔ This attention mechanism allows the model to focus on different parts of the source sentences dynamically while generating the translation.

Attention mechanism :

 ✅The Attention mechanism is a technique in AI that helps models focus on specific parts of the input when generating the output. By assigning different weights to different input elements based on their relevance, the mechanism improves the model's ability to understand context and prioritize imp information

✔ Also addressed the problem of learning alignment between input and output sequences ,enables the model to weight the imp of word in the source sentence differently during translation. By dynamically adjusting the attention weights , the model can focus more on relevant words and ignore irrelevant words, leading to more accurate translation. Adding the attention weights ,attention mechanism has improved the quality of translation on long input sentences.

Drawbacks of  Attention mechanism:

• By using the LSTM architecture ,training is happing sequence , due to that sequence training model taking lots of time to generate the output and also showing some long term dependence on large sentences.

Introduction to Transformers:

✅Attention is all you need  paper came in 2017 , based on attention mechanism ,this paper introduced by Google which  solves the sequential training problem and long term dependence introducing the Transformers.

✅Transformer are  the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention. 

✅For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English to French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.

Key features of Transformers:

   Transformer has the encoder-decoder architecture.

• Encoder (great at understanding text)
• Decoder ( great at generating text)
• Positional Encoding
• Self-Attention Mechanism
• Multi-Head Attention
• Feed-Forward Network
• Skip/Residual Connections
• Parallelization and Scalability

Why Transformers :

✅Transformers are the type of deep learning model introduced in the paper "Attention is All You Need". Transformers have found numerous applications across various fields due to their ability to handle sequential data efficiently.

• Scalable and Parallel Compute
• Revolutionized NLP with LLMs
• Unification of DL Approaches for text, images, audio and video datasets
• Multi-Modal Capability
• Accelerated GenAI

1. Scalable and Parallel Compute:  

Transformers architecture allows for parallelization during training , which significantly improves efficiency and scalability , especially for large datasets  and complex models.

2. Revolutionized NLP with LLMs : 

Transformers have revolutionized NLP by enabling the development of large language models(LLMs) like BERT and GPT , which have set new performance benchmarks for  many NLP tasks.

3. Unification of DL Approaches:

Transformer provides a unified architecture that can be adapted for different types of data,  such as text, images, audio and video allowing for a cohesive approaches across various domains.

4. Multi-Modal Capability :

Multimodality means the ability to take in multiple of  inputs 9text, image, audio and video ) and generates multiple types of outputs. Example GPT-4 

5. Accelerated GenAI:

Transformers  have boosted generative AI by helping to create models that can produce clear relevant text, images, music and other types of content.

 

Transformers are widely using in various domains those are:

1.Natural Language Processing:

• Language Translation
• Question Answering
• Named Entity Recognition (NER)
• Chatbot
• Text summarization

2.Image Recognition

• Object Detection
• Image Classification

3.Speech Recognition and Synthesis

Components of Transformers:

Transformer has the encoder-decoder architecture.

• Encoder is great at understanding text.
• Decoder is great at generating text.
• Transformer relies solely on self-attention mechanisms and feed-forward neural networks.
• Understand that Attention is a mechanism that assigns different weights to different parts of the input allowing the model to prioritize and emphasize the most important information while performing tasks like translation or summarization. Attention allows a model to focus on different parts of the input dynamically, leading to improved performance.
• Positional Encoding: To retain positional information of words in the input sequence without using recurrence, the model introduces positional encodings. These encodings are added to the input embeddings to provide information about the position of each word in the sequence.
• Self-Attention Mechanism: The key innovation of the Transformer is the self-attention mechanism, which allows each word in the input sequence to attend to all other words in the sequence. This enables capturing global dependencies and alleviates the need for recurrent connections.
• Multi-Head Attention: The Transformer employs multi-head attention mechanisms, where attention is computed multiple times in parallel with different learned linear projections. This allows the model to focus on different parts of the input sequence simultaneously, enhancing its ability to capture diverse patterns.
• Feed-Forward Network: The main goal of the feed-forward network is to apply non-linear transformations to the input representations, helping the model capture complex patterns and relationships in the input sequences. This helps enriching the representations of words or tokens in the input sequence.
• Skip/Residual Connections: The main goal of skip connections is to enable the network to retain important information from previous layers and make it easier for the model to learn and optimize complex patterns in the data. Think of skip connections as shortcuts that allow information to bypass certain layers in the network. These shortcuts ensure that important information from earlier layers is preserved and remains accessible to later layers.
• Parallelization and Scalability: By relying on self-attention mechanisms and feed-forward layers, the Transformer architecture facilitates parallelization of computation across different parts of the input sequence. This results in faster training times and better scalability compared to traditional recurrent models.

✔These components work together to form the transformer architecture , enabling it to process sequence of data efficiently and effectively for various tasks such as Machine Translation, Text Summarization and other NLP applications.

Trained of GPT-1 from Scratch :

Training of GPT-1 from scratch involves several steps:

1. Data Collection & Preprocessing

2. Model Architecture

3. Training the Model

4. Fine Tuning (if needed)

5. Evaluation

This above steps are involved to built a GPT-1, 

👀Let's  follow the steps:

1. Data Collection & Preprocessing :

 Data collection : For training the GPT-1 requires large amount of data , which is relevant to train GPT-1 in easier manner. Huge data might contains from various aspects like books, articles, websites, google books etc.

Preprocessing :Collected data might have irrelevant content, that would be removed form data by applying the different preprocessing techniques to resolve low performance of model.

2. Model Architecture :

Define the Model : GPT-1 is based on the Transformers architecture and consists  of multiple layers are self-attention and feed-forward neural networks.

Embedding layer :The model starts with an embedding layer that converts input token into continuous vector representation.

Transformer layer :GPT-1 has multiple transformers layers, each consisting of multi-head self-attention and feed-forward neural networks with residual connections and layer normalization.

Output layer; The final layer of the model produces predictions for the next token in the sequence.

3. Training the Model:

Objective: GPT-1 is trained  using a language modeling objective, where the goal to predict the next token in a sequence based on the previous tokens.

Backpropagation: The Model can be get less error by applying the backpropagation, where weights and get updated , by backpropagation weights will learn the relation between the tokens.

Optimization : State of the Art optimizer is Adam, which is used to update the weights and minimize the error and increase the learning rate.

4. Fine Tuning (if needed):

Fine tuning is needed  to increase the performance of model and minimize the loss function by good fine tuning on data

5. Evaluation:

The model performance is measured using the different metrics, based on evaluation score model will go on application server.

References:

• Innomatics Research Labs
• Sequence to Sequence Learning with Neural Networks paper
• Neural Machine Translation by Joint Learning to Align and Translate paper
• Attention is all you need paper

Thank you 🙏