The Components of a Large Language Model (LLM)

Large Language Models (LLMs) represent a remarkable leap in the field of artificial intelligence (AI), enabling machines to understand and generate human-like language with unprecedented accuracy. These models have revolutionized how we interact with technology, driving advancements in natural language processing (NLP) applications such as chatbots, translation services, content creation, and much more. At their core, LLMs are powered by sophisticated algorithms and massive datasets, allowing them to learn the intricacies of human language. In working with the Smarter Consulting team to develop new M365 Governance and Copilot Readiness training and consulting services, I thought I’d outline some of the basics surrounding working with generative AI solutions (like ChatGPT) and enterprise AI solutions (like Microsoft Copilot) to help people understand how these solutions work. For this three-part blog series, I thought I’d begin by demystifying the workings of LLMs, offering a comprehensive overview of their components, share some resources on self-training, and discuss the common mistakes and best practices for building your own LLM.

Components of an LLM

Understanding the technology we use is crucial to harnessing its full potential. By familiarizing ourselves with the components of Large Language Models (LLMs), we can better appreciate their capabilities and apply them more effectively. Here is a breakdown of the key elements that make LLMs function:

• Training Phase: The foundation of any LLM lies in its training phase, where it is exposed to vast amounts of text data sourced from books, articles, websites, and various other written materials. This extensive dataset provides the model with a rich tapestry of language from which it can learn grammar, context, facts, and even some reasoning abilities. During training, the model iteratively adjusts its internal parameters to minimize errors in predicting the next word or token in a sequence. This process, known as supervised learning, involves backpropagation and gradient descent to fine-tune the model’s predictions.
• Neural Networks: At the heart of LLMs are neural networks, with transformers being the most prominent type used today. Transformers consist of layers of interconnected nodes, or neurons, that process information in a manner analogous to the human brain. Unlike traditional neural networks, transformers leverage self-attention mechanisms, allowing them to weigh the importance of different words in a sentence relative to each other. This architecture enables transformers to capture long-range dependencies and intricate relationships within the text, making them highly effective for NLP tasks.
• Tokenization: Tokenization is a crucial preprocessing step in training LLMs. During tokenization, text is broken down into smaller units called tokens, which can be words, characters, or subwords. The choice of tokenization method impacts the model’s ability to understand and generate text. For instance, subword tokenization allows the model to handle out-of-vocabulary words by breaking them into known subwords. By predicting the next token in a sequence, the model learns to generate coherent and contextually appropriate text, even for complex or unseen phrases.
• Contextual Understanding: One of the defining features of transformers is their use of the attention mechanism, which facilitates contextual understanding. Attention allows the model to focus on different parts of the input text, assigning varying levels of importance to each word based on its relevance to the current context. This capability enables the model to grasp nuanced meanings and relationships within the text, leading to more accurate and context-aware responses. For example, in the sentence “The cat sat on the mat,” attention helps the model understand that “cat” and “mat” are related, while also considering the role of “sat” in the overall meaning.
• Inference Phase: Once trained, the LLM enters the inference phase, where it generates text based on a given prompt. During inference, the model leverages the patterns and knowledge acquired during training to predict and produce coherent and contextually appropriate continuations of the input text. The inference process involves sampling from the model’s probability distribution over possible next tokens, which can be fine-tuned using techniques like beam search or top-k sampling to balance creativity and coherence.
• Fine-Tuning: To enhance performance in specific domains or tasks, LLMs can undergo fine-tuning. Fine-tuning involves additional training on a smaller, specialized dataset, allowing the model to adapt to particular contexts, such as medical terminology, legal language, or creative writing. This targeted training refines the model’s predictions and improves its accuracy and relevance for specialized applications. Fine-tuning is a powerful technique that extends the versatility of LLMs, making them suitable for a wide range of use cases.
• Applications: The versatility of LLMs has led to their widespread adoption across various applications. They power chatbots, enabling natural and engaging conversations with users. In translation services, LLMs facilitate accurate and contextually appropriate translations between languages. Content creation tools leverage LLMs to generate articles, summaries, and creative writing, while other applications include sentiment analysis, code generation, and more. By understanding these key components, students can grasp how LLMs mimic human language processing to generate meaningful and contextually relevant text.

By delving into the inner workings of LLMs, we gain a deeper appreciation for the complexity and power of these models. In the next part of this series, we will explore how to build your own LLM, offering practical guidance and insights into the tools and techniques required. Following that, we’ll discuss common pitfalls and mistakes made when developing LLMs, providing valuable lessons to ensure successful implementation. In Part 2 of this series, I’ll share some free online resources to help you learn to build your own LLMs and get more out of generative AI solutions.