Let’s cover some key concepts
Natural language processing (NLP) is a branch of artificial intelligence that deals with the interaction between computers and human languages. LLM stands for large language model, which is a type of neural network that can learn from large amounts of text data and generate natural language outputs. GPT (one of the most common transformer models) stands for Generative Pre-trained Transformer.
A transformer is a type of Neural Network that can process sequential data, such as natural language, by using a mechanism called self-attention (this part is key, which we’ll cover later).
Before we cover the Transformer architecture, lets look at parameters.
What is a Transformer? “Attention is All You Need”
The “transformer” is a type of model architecture used in the field of deep learning, particularly for tasks involving natural language processing (NLP). It was introduced by Vaswani et al. in a 2017 paper titled “Attention is All You Need”. Since then, numerous variations and improvements upon the original transformer model have been introduced.
- Standard Transformer: Introduced in the “Attention is All You Need” paper. The standard transformer model uses a mechanism called self-attention (or scaled dot-product attention) and consists of an encoder-decoder structure.
- BERT (Bidirectional Encoder Representations from Transformers): Developed by Google, BERT is a transformer used for text classification tasks that reads the entire sequence of words at once, making it bidirectional. This allows the model to learn context from both past and future words in the text.
- GPT (Generative Pretrained Transformer): Developed by OpenAI, GPT is a large-scale, unsupervised, transformer-based language model. Unlike BERT, it’s an autoregressive model that generates text sequentially from left to right.
- Transformer-XL (Transformer with Extra Long context): This variant introduces a recurrence mechanism to the Transformer model to enable it to handle longer-term dependencies, making it more suitable for tasks such as text generation.
- RoBERTa (Robustly Optimized BERT approach): RoBERTa is a variant of BERT that modifies key hyperparameters in the model architecture and the training approach. It removes the next-sentence pretraining objective and trains with much larger mini-batches and learning rates.
- T5 (Text-to-Text Transfer Transformer): T5 is a transformer model from Google that casts all NLP tasks into a unified text-to-text-format. This allows the model to use the same approach to handle different tasks, such as translation, summarization, and classification.
- DistilBERT: This is a smaller, faster, cheaper, and lighter version of BERT. It retains 95% of BERT’s performance while being 60% smaller and 60% faster.
- ALBERT (A Lite BERT): ALBERT is another variant of BERT that reduces the model size (but not the model architecture) by sharing parameters between layers. It also introduces a new self-supervised loss for sentence-order prediction.
- The list goes on………. (Hugging Face, Lama etc)
During inference, the Transformer model takes in an input sequence (e.g., a sentence in natural language) and generates a corresponding output sequence (e.g., a translated sentence in a different language). The attention mechanism in the Transformer model helps it focus on the most relevant parts of the input sequence to generate more accurate output sequences.
What makes up a transformer architecture?
- Tokens: In the context of natural language processing (NLP) and transformers, tokens are the smallest units of language that a model can understand and process. They can range from a single character to a whole word or even more in some languages.
- Embeddings: Once our text is broken into tokens, we need a way to represent these tokens numerically, so the model can process them. This is done through embeddings, which are learned by the model during training. Embeddings represent tokens as vectors in a high-dimensional space (certainly beyond the scope of this post!) where similar words have similar embeddings.
- Positional Encoding: In addition to the token embeddings, Transformers use positional encodings to capture the order of words in a sentence. This is important because unlike models like RNNs and LSTMs, Transformers do not process tokens sequentially, so they need another way to understand word order.
- Self-Attention Mechanism: This is a key part of the Transformer architecture. It allows the model to weigh the importance of each token in the context of every other token in the sentence. It helps the model understand the context and relationships between words.
- Layers: The Transformer model is made up of multiple layers, each consisting of a self-attention mechanism and a feed-forward neural network. The output from one layer is fed as input to the next, allowing the model to learn complex relationships between tokens.
- Training and Fine-Tuning: Transformers are trained in two steps. First, they are pre-trained on a large-scale dataset to learn general language understanding. During this phase, the model learns both weights and embeddings. Then, they are fine-tuned on a smaller, task-specific dataset. During fine-tuning, the model updates its weights and embeddings to better suit the specific task.
- Token Limit: Transformers have a maximum sequence length, or token limit, due to the self-attention mechanism which increases computational cost with the number of tokens. This is a fundamental aspect of the architecture and is something to consider when working with these models.
Parameters in a machine learning model, including Transformers, are the parts of the model that are learned from the data during training. In Transformers, there are two main types of parameters: weights and biases.
- Weights: These are the values that determine how much each input feature, in this case, the value of each element in the embeddings contributes to the output. In the self-attention mechanism, for instance, weights are used to calculate the attention scores. These scores are essentially the weights assigned to each word when considering its influence on other words. The weights in the model are adjusted during training to minimize the difference between the model’s predictions and the actual values.
- Biases: These are additional parameters that are added to the outputs of the weighted sum of inputs. They allow the output to be shifted by a constant value, regardless of the input values. Like weights, biases are also learned during training.
The combination of weights and biases forms the learned parameters of the model. The learning process involves iteratively adjusting these parameters to reduce the model’s error on the training data. Once the model is trained, these parameters are used to make predictions on new, unseen data.
In a Transformer model, weights and biases are present in various parts, including the self-attention layers and the feed-forward neural networks. The embeddings are also parameters of the model that are learned during training.
In large Transformer models, there can be hundreds of millions or even billions of parameters. This large number of parameters is part of what allows these models to capture complex patterns in the data, but it also makes them computationally intensive to train and requires a lot of data to avoid overfitting.
The first GPT model was introduced in 2018 and had 117 million parameters. Since then, OpenAI has released several improved versions of GPT with more parameters and capabilities, such as GPT-2 (1.5 billion parameters), GPT-3 (175 billion parameters), and GPT-4 (50 trillion parameters)3. Other organizations have also created their own GPT-inspired models, such as EleutherAI’s GPT-Neo (2.7 billion parameters), Cerebras’ CS-1 (120 trillion parameters), Salesforce’s EinsteinGPT (for CRM), and Bloomberg’s BloombergGPT (for finance).
Why have transformers have been a game changer for LLMs?
- Scalability: One of the main advantages of transformers is their scalability. They can be parallelized across multiple GPUs, which allows for the training of much larger models compared to traditional recurrent neural networks (RNNs). Transformers have led to state-of-the-art performance in a wide range of NLP tasks, such as machine translation, sentiment analysis, and question-answering systems.
- Transfer learning: (Illustrated Below), (we cover this in much greater detail here) Transformers can be pre-trained on large amounts of text data and fine-tuned for specific tasks. This has made it easier to develop high-performing models for different applications with relatively small amounts of task-specific data.
- Their versatility has paved the way: Transformers have paved the way for LLMs like GPT and BERT (Bidirectional Encoder Representations from Transformers). These models have demonstrated remarkable abilities in understanding and generating human-like text.
- Real-world applications: Transformers have led to numerous real-world applications, such as chatbots, virtual assistants, content generation, and many others, making them an essential part of the AI landscape.
Making an informed decision
One can certainly develop a language model without understanding (or deep knowledge) of these details. However, having an understanding can help you solve problems, make more informed decisions, and potentially create more effective models.
If you’re looking to build your own language model, or start with a pre-trained model, understanding the Transformer architecture can be incredibly helpful. Whether it’s for problem-solving, parameter tuning, model customization, or just staying up-to-date with the latest in NLP, knowing the inner workings of these models can provide valuable insights.
Furthermore, if you have enterprise data that you’d like to leverage, you can fine-tune a pretrained model on your specific data or even train a model from scratch. You can also combine structured and unstructured data, create a knowledge graph, or employ a hybrid approach depending on the nature of your data and the specific use case you have in mind
LLMs are powerful AI models that have demonstrated remarkable capabilities in natural language processing and other domains. Their success is due in large part to the Transformer architecture, which enables the models to effectively capture long-range dependencies in sequential data such as text.
In the Next post we’ll take a look a LLM pre-training types and techniques.
Check Out the Generative AI 101 Blog Series:
Blogs Coming Up Next:
- Generative AI 101 Part 2: How are LLM’s Trained?
- Generative AI 101 Part 3: Pre-Trained Model Fine Tuning and Transfer Learning
- Generative AI 101 Part 4: Inferencing (Running your LLM)
- Generative AI 101 Part 5: Project Helix Dell and NVIDIA Solution Architecture