Welcome to an in-depth Introduction to Generative AI Models, a fascinating field that’s rapidly transforming how we interact with technology and create content. Unlike traditional models that analyze existing data, generative models aim to create new data that resembles the input they were trained on. This article will break down the core concepts, applications, and the powerful neural network architectures behind these models.
What are Generative AI Models?
Generative AI models represent a significant leap from simpler parametric models like Gaussians. They leverage the expressive power of neural networks to handle highly complex datasets, including images, text, programs, and even molecules. Recent advancements in this area include sophisticated techniques like diffusion and flow models, as well as transformer models.
Real-World Applications of Generative Models
The impact of generative models is already visible across various domains. Here are some notable examples:
- Protein Design: Models like RFDiffusion can de novo design protein structures and functions, opening new avenues in biotechnology.
- Image Synthesis: StableDiffusion allows for high-resolution image synthesis, revolutionizing digital art and media creation.
- Chemical Reaction Prediction: Molecular Transformers can predict chemical reactions with uncertainty calibration, accelerating drug discovery and material science.
Generative Modeling with Neural Networks: Focusing on Language
One of the most prominent applications of generative models is in natural language processing. The basic idea behind large language models (LLMs) is next “word” prediction. This process uses a technique called “self-supervised learning,” where the model learns to predict the next element in a sequence based on the preceding elements.
Probabilistic Models Over Sentences
Imagine we want to learn a probabilistic model over sentences of a fixed length. Each word can be treated as a categorical random variable, ranging over a defined vocabulary. The challenge lies in representing the joint distribution of these words. For instance, with 'm' binary random variables (like words), specifying P(X1,..., Xm) requires an exponentially large number of parameters (2^m - 1 to be precise), quickly becoming intractable for real-world vocabularies.
Factorizing Joint Distributions
The key to managing this complexity is to factorize the joint distribution using the product rule: P(X1=x1,..., Xm=xm) = P(X1=x1) P(X2=x2|X1=x1)... P(Xm=xm|X1:m-1=x1:m-1). If we can estimate these conditional probabilities, we can then generate samples from the joint distribution, effectively creating new sentences word by word.
The Rise of Neural Network-Based Language Models
The central idea in modern language models is to estimate the conditional distribution P(X_t | X_<t) from data using a neural network. Specifically, this is often done by approximating P(X_t = x | X_t-k:t-1 = x_t-k:t-1, θ) using a categorical distribution with probabilities derived from a softmax function applied to the output of a neural network f(x_t-k:t-1, θ). Here, 'k' is the context length, meaning the model predicts the next word from the previous 'k' words.
Autoregressive Training and Self-Supervision
This process is trained by minimizing the cross-entropy loss, where the next word acts as a “label”—a technique known as autoregressive training and self-supervision.
Neural Network Architectures for Sequence Modeling
Historically, various neural network architectures have been used for sequence modeling:
- Recurrent Neural Networks (RNNs): Including LSTMs and GRUs, designed to handle dependencies over long sequences.
- Transformer Models: These have become the dominant architecture in modern language models, used for generation, translation, and increasingly in computer vision.
Delving into Transformer Models
The Transformer Neural Network model, introduced in “Attention is All You Need” [Vaswani et al., 2017], is a specialized architecture capable of learning “input-dependent dependencies” through its attention mechanism.
From Encoder-Decoder to Decoder-Only
The original Transformer used an encoder-decoder architecture for sequence-to-sequence tasks like machine translation. However, modern language models often employ a simpler, decoder-only variant, as seen in the Generative Pretrained Transformer (GPT).
The Generative Pretrained Transformer (GPT)
GPT models represent word (token) indices for a context of k previous words (X). They incorporate two key embeddings:
- Word Embeddings (W_e): Each word is represented by a learnable feature vector.
- Positional Embeddings (W_p): These fixed embeddings provide a vectorial encoding of word positions, often using periodic functions, and have the same dimension as word embeddings.
The computational flow involves combining word and positional embeddings, passing them through transformer blocks, and then using a softmax layer to predict the next word's probability.
The Transformer Block and Masked Multi-Head Self-Attention
The core of the transformer block is the “masked multi-head self-attention” mechanism. This allows words (tokens) to