Basics : Deep Learning : LLMs and Transformers Inside View

St Germain en Laye, November 29th 2024.

 

Large Language Models (LLMs), like GPT-4, and Transformer architectures are foundational technologies in modern natural language processing (NLP). They are designed to process and generate human-like text based on patterns learned from large datasets. Here’s a breakdown of how they work:

The Transformer Architecture

Transformers, introduced in the paper « Attention is All You Need » (Vaswani et al., 2017), are the backbone of modern LLMs. The key innovation of Transformers is the self-attention mechanism, which allows the model to process input data in parallel and understand the relationships between words (or tokens) in a sequence, no matter how far apart they are.

• Key Components of Transformers:
  • Self-Attention: This is the core idea of Transformers. Each token in an input sequence attends to (i.e., focuses on) every other token, allowing the model to capture dependencies between distant words. For example, in the sentence « The cat sat on the mat, » the model can learn that « cat » and « sat » are related, even though they are not next to each other.
    • The model computes three vectors for each word (token):
      • Query (Q): Represents the word’s request for information.
      • Key (K): Represents the information that is available in the word.
      • Value (V): The actual information the word can share.
    • The self-attention mechanism compares the Query vector with all Key vectors in the sequence to determine which tokens should influence the current token. The result is a weighted sum of the Value vectors, which is then used to represent the token in the context of the entire sentence.
  • Positional Encoding: Transformers don’t process sequences in order (like RNNs or LSTMs). Instead, they process all tokens in parallel. To give the model a sense of the order of words, positional encodings are added to the input embeddings, which specify the position of each token in the sequence.
  • Feedforward Networks: After self-attention is applied, the output goes through a fully connected feedforward network (usually consisting of two linear transformations with a ReLU activation in between).
  • Layer Normalization and Residual Connections: To ensure stable training, residual connections (shortcuts) are added around the attention and feedforward layers, and layer normalization is applied to the output of each layer to stabilize gradients.
  • Multi-Head Attention: Instead of computing a single attention score, the Transformer computes multiple sets of attention scores (with different weights), allowing it to focus on different aspects of the input simultaneously.

 

• Architecture Overview:
  • The Transformer model consists of two main parts:
    • Encoder: The encoder processes the input sequence and generates a set of context-aware representations of each token. In tasks like translation, the encoder would convert the source language into a representation that the decoder can use.
    • Decoder: The decoder generates the output sequence, conditioned on the encoder’s output (in sequence generation tasks like text generation). In models like GPT, which are autoregressive, the decoder is used to generate text step-by-step.

 

Training Large Language Models (LLMs)

LLMs like GPT-3, GPT-4, and BERT are based on Transformer architectures but are designed to scale up to massive datasets and millions (or even billions) of parameters.

• Pretraining:
  • Autoregressive Pretraining (for GPT-like models): In autoregressive models, the model is trained to predict the next word in a sequence given the previous words. For example, if the input is « The cat sat on the ___, » the model learns to predict the next word, « mat. »
  • Masked Language Modeling (for BERT-like models): In contrast to autoregressive training, BERT (Bidirectional Encoder Representations from Transformers) is trained using a technique called masked language modeling. In this setup, random words are masked (replaced with a special token), and the model is tasked with predicting the masked words based on the surrounding context. This allows the model to learn bidirectional relationships in text.

Both types of pretraining require massive amounts of data, such as books, websites, and other text sources, to capture a wide range of linguistic patterns and knowledge.

• Fine-Tuning:

After pretraining, the model is fine-tuned on specific tasks (like sentiment analysis, machine translation, or text summarization) using labeled datasets. Fine-tuning adjusts the model’s parameters to specialize in the target task while leveraging the general knowledge learned during pretraining.

 

Generative vs. Discriminative Models

• Generative Models (e.g., GPT): These models generate text by predicting the next token given previous tokens. They are autoregressive in nature, meaning they generate tokens one at a time and use their own previous predictions as part of the context for generating subsequent tokens. This is why GPT models are good at generating long passages of coherent text.
• Discriminative Models (e.g., BERT): These models are trained to predict a label for a given input, typically used for tasks like classification, token labeling, and sentence-pair tasks. They are not autoregressive and do not generate text, but they are good at understanding the relationships between words in a sentence.

 

How LLMs Perform Tasks

Once trained, LLMs can perform a wide range of NLP tasks, including:

• Text Generation: Given a prompt, the model generates coherent and contextually appropriate text (e.g., story generation, code completion).
• Text Classification: Assigning categories to text, such as sentiment analysis, topic classification, etc.
• Named Entity Recognition (NER): Identifying named entities like people, locations, and organizations within text.
• Question Answering: Given a context (e.g., a paragraph), the model can answer questions about that context.
• Translation: Translating text from one language to another.

The key to their performance is the pretraining on vast amounts of data, which helps the model learn general language patterns, and fine-tuning on specific tasks to make it more useful in a given domain.

 

Scaling Up and Challenges

LLMs have continued to scale up in size, with models like GPT-3 and GPT-4 containing billions (or even trillions) of parameters. Larger models generally have better performance but also come with challenges such as:

• Computational Cost: Training large models requires massive computational resources, often requiring specialized hardware like GPUs or TPUs.
• Data Biases: The models can inherit biases from the data they were trained on, leading to ethical concerns in their application.
• Interpretability: Understanding how large models make decisions is a challenging area of research, often referred to as the « black-box » problem.

Despite these challenges, the Transformer architecture has proven to be highly effective, and LLMs like GPT-4 are at the forefront of AI-driven language understanding and generation.

 

Summary

• Transformers use self-attention to capture relationships between tokens in a sequence, allowing for parallel processing of text and capturing long-range dependencies.
• LLMs are trained on vast amounts of data and fine-tuned for specific tasks. They can generate and understand text, making them versatile in a wide range of NLP applications.

 

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