Hello everyone! In this article, we'll dive into the fascinating world of large language models, exploring the mechanics behind features like autocomplete on your mobile phone and how search engines generate suggestions. We'll also delve into the challenges of language modeling, the power of neural networks, and how they can be used to create powerful language models capable of generating poetry, translating languages, and even writing computer code. Let's get started!

Part 1: Introduction to Large Language Models (LLMs)

Understanding Autocomplete and Frequency-Based Language Modeling

Have you ever wondered how the autocomplete feature on your mobile phone works? When you start typing a word, your phone suggests the most commonly used word based on the characters you've entered. This process relies on calculating word frequencies and sorting them to identify the most likely next word.

While frequency-based language modeling is helpful for generating suggestions, it has its limitations when it comes to generating original sentences. With the vast number of possible word combinations, it's impossible to simply count the frequency of all possible sentences to determine the probability of a sentence occurring. We need a more sophisticated approach to model language effectively.

Modelling Language with Graphs and Conditional Probabilities

To better understand language modeling, let's consider an example using lyrics from a Nobel Prize-winning poet, Bob Dylan. By treating the text as a time series and graph, we can analyze the dependencies between words and generate new phrases that resemble Dylan's style.

However, this method is not perfect. The model often produces nonsensical results, and it's clear that a more complex model is needed to accurately represent language. We need to consider factors such as grammar, style, and long-range dependencies between words.

The Power of Neural Networks in Language Modeling

Neural networks offer a promising solution for approximating complex functions and modeling language. As universal approximators, neural networks can learn to approximate almost any function with enough capacity and training.

When designing a neural network for language modeling, we need to consider factors such as the network's capacity and the choice of activation functions. A well-designed neural network can model the complex relationships between words, enabling the generation of coherent sentences and phrases.

Training Neural Networks with Gradient Descent and Backpropagation

To train a neural network, we use a process called gradient descent. This involves calculating the gradients of the error function and adjusting the weights of the network to minimize the error. The process of backpropagation allows us to compute the partial derivatives of the error function efficiently, serving as the workhorse of neural network training.

However, finding the right network design and capacity is essential for effective language modeling. A network with insufficient capacity or an inappropriate activation function may struggle to model the complexities of language accurately.

Part 2: How Neural Networks Work With LLMs

Neural networks are designed to approximate complex functions, making them useful tools for language modeling. In this article, we will explore how neural networks can be applied to model language, focusing on their role in predicting words, understanding semantic relationships, and generating text. We will also delve into the architecture of neural networks and how they can be trained to perform these tasks.

Word Embeddings

Before we can use a neural network to model language, we must first convert the words into numerical representations that the network can understand. One effective method for doing this is called word embeddings, which map semantically similar words to similar numerical values or vectors. Word embeddings are widely available online and serve as a foundation for building language models using neural networks.

Designing a Language Neural Network

When designing a language neural network, it is essential to consider the network's architecture, capacity, and the choice of activation functions. The challenge in modeling language lies in accounting for the complex relationships and long-range dependencies between words. To address this issue, a transformer network can be employed, which combines an attention network with a prediction network.

Transformer Networks and the Attention Mechanism

Transformer networks are designed to handle the intricate dependencies between words by focusing on a subset of the words. An attention network assigns attention weights to each word, which are then multiplied by the words themselves and fed into the prediction network. This method allows the transformer to train both networks simultaneously, with the prediction network informing the attention network on what it needs to learn for better next-word prediction.

In practice, the attention network operates one word at a time, estimating the relationships between the target word and other words in the text. These attention scores, ranging between 0 and 1, are used to generate a weighted sum of the words, resulting in a context vector. Each word receives its own context vector, which is then fed into the prediction network alongside the original words.

Generating Text with Transformer Networks

To generate text using a transformer network, a starting word is chosen and fed through the network. The network then predicts the next word, often providing multiple suggestions with different probabilities. By selecting one of the top predictions and adding it as the next input, the network can generate subsequent words in a coherent manner.

Scaling Up Network Capacity

For a neural network to answer questions or generate text based on a wider range of topics, it requires a significant increase in capacity. This can be achieved by stacking multiple attention and prediction layers. For instance, GPT-3, a popular model developed by OpenAI, has 96 layers and hundreds of billions of parameters, enabling it to perform a diverse array of tasks.

As the network's capacity increases, lower layers focus on word relationships and syntax, while higher layers encode more complex semantic relationships.

Training Language Models on Massive Text Corpora

To train large language models, vast amounts of text data are required. Modern models like GPT-3 have been trained on most of the internet and publicly available books, amount ing to approximately half a trillion words. Training these models is a computationally intensive process that would take several years on a single GPU. However, transformer networks are designed to be highly parallelizable, allowing them to be trained in about a month using thousands of GPUs.

Applications and Limitations of Large Language Models

Large language models like GPT-3 can perform an impressive range of tasks, including answering factual questions, solving problems, generating poetry, translating languages, creating new recipes, and even writing code. However, they are not perfect and can struggle with basic arithmetic, spatial relationships, and fact-checking. Additionally, these models can suffer from bias and stereotyping, highlighting the need for ongoing research in this area.

Despite their limitations, large language models have shown great potential in various applications, demonstrating the ability to generate creative content, understand complex relationships, and perform high-level reasoning.

Part 3 - The Inner Workings of Large Language Models

But what exactly goes on under the hood of these complex AI systems? Let's explore some of the key components that give large language models their impressive natural language capabilities.

Neural Network Layers

LLMs are composed of multiple different neural network layers that each serve a specific purpose. These layers work together to digest text input and generate coherent outputs.

Embedding Layers

The embedding layer is responsible for encoding the input text into vector representations that capture semantic and syntactic meaning. This gives the LLM a machine-readable way to comprehend the context provided.

Feedforward Layers

Feedforward layers then transform these input embeddings into higher-level abstract concepts. This allows the model to interpret the intent behind provided text.

Recurrent Layers

Recurrent layers process input words sequentially. This enables the model to understand relationships between words and their order in sentences.

Attention Layers

Attention layers let the model focus on the parts of input text most relevant to the current task. This improves accuracy of outputs.

Tuning Approaches

In addition to architecture, how LLMs are tuned during training shapes their capabilities:

Generic Pretraining

Generic or raw LLMs are exposed to diverse text data to build general linguistic mastery. This suits them for broad information retrieval.

Instruction Tuning

Instruction-tuned LLMs are trained to follow specific instructions and prompts. This tuning produces skills like text generation, summarization, and analysis.

Dialog Tuning

Dialog-tuned LLMs are optimized for conversational ability through exposure to dialog examples. This enables applications like chatbots.

So in summary, large language models derive natural language proficiency from specialized neural network architectures combined with expansive training datasets. Components like embedding layers, transformers, and tuning procedures equip LLMs for diverse AI applications.


Neural networks, particularly transformer networks, have revolutionized the field of language modeling. By harnessing the power of attention mechanisms and increasing network capacity, these models can generate coherent text, understand semantic relationships, and perform a wide range of tasks. While there are still challenges to overcome, such as biases and limitations in reasoning, the advancements made in this field continue to push the boundaries of what language models can achieve.


These videos were invaluable in my understanding of LLMs for this article

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