Understanding Large Language Models (LLMs)
A Library Analogy
Imagine stepping into a vast, grand library. This isn’t just any library—it’s one filled with endless books, each holding the secrets to virtually every topic imaginable. From ancient history to futuristic science, from poetry to technical manuals, this library has it all. Now, imagine that this library isn’t made of physical books but of words, ideas, and knowledge stored in a digital format. This is the essence of a Large Language Model (LLM), an AI system designed to understand, generate, and interact using human language. Let’s explore this analogy to understand what an LLM is and how it works.
The Researcher (You):
Picture yourself as a researcher tasked with writing a report on how to pick stocks like Warren Buffett. The topic is complex, and the information you need is scattered across countless sources. To make your job easier, you decide to hire an assistant—a highly skilled one that can sift through mountains of data in seconds. This assistant is your gateway to the library, and in the world of AI, it’s the Large Language Model.
The Library (The LLM):
Your assistant heads to a grand library, a place so vast that it contains millions of books on every subject imaginable. This library represents the LLM, which has been trained on an enormous amount of text data—books, articles, websites, and more. Just as the library’s shelves are filled with knowledge, the LLM’s "mind" is filled with patterns, relationships, and insights derived from its training data.
The Librarian (The Prompt Interface):
Upon arriving at the library, your assistant approaches the librarian with a request: "Find me information on how to invest like Warren Buffett." The librarian, who represents the prompt interface of the LLM, listens carefully and interprets your request. Their job is to figure out the best way to navigate the library’s vast collection to find exactly what you need.
The Card Catalog (Parsing the Data):
The librarian consults the card catalog, a system that organizes books by subject, author, and title. In the LLM, this is akin to parsing and organizing data. The model breaks down your request into smaller, manageable pieces, categorizing them to understand the context and intent behind your query.
Tokens: The Building Blocks of Language:
Imagine a toddler trying to read a book title, breaking it into smaller parts. For example, the word "computer" might become "COM-PUT-TER." Each of these parts is a token, the basic building block of language in an LLM. Tokens are like the individual bricks used to construct a house. But here’s the catch: LLMs don’t understand words—they only understand numbers.
To bridge this gap, words are converted into tokens, which are then assigned numerical values. Think of it like a library where every book title is replaced with a unique ISBN number. For instance, the word "cat" might be tokenized as 1234, while "dog" becomes 5678. These numbers allow the LLM to process and analyze language mathematically.
Tokenization is the first step in transforming human-readable text into a format the model can work with. Depending on the language and complexity, a single word might be split into multiple tokens. For example, the word "unhappiness" could be broken into three tokens: "un," "happi," and "ness." Each token is assigned a number, and these numbers are what the LLM uses to perform its calculations and generate responses.
Vectors: Navigating the Library:
When searching for a book, the librarian might use criteria like the author’s name, the publisher, or the subject. In an LLM, these criteria are represented as vectors—mathematical constructs that help the model navigate its knowledge base. Vectors allow the LLM to find relationships between words, concepts, and ideas, much like the librarian uses metadata to locate the right book.
Embeddings: Summarizing the Books:
Each book in the library comes with a summary that captures its essence. In the LLM, these summaries are called embeddings. Embeddings are numerical representations of words or phrases that capture their meaning and context. For example, the word "queen" might refer to a monarch or the band Queen. Embeddings help the LLM determine the correct meaning based on the surrounding context and decide where to place the word or embedding in the matrix.
The Stacks (The Matrix):
The librarian searches through various stacks, floors, and shelves to find the books you need. Similarly, an LLM searches through a matrix—a three-dimensional grid of data—to find relevant information. Think of the matrix as a massive three-dimensional Excel spreadsheet where words, phrases, and concepts are stored and organized.
Words or embeddings are placed in the matrix depending on their context. For example, the words "queen," "king," and "knight" might be grouped together in one area of the matrix because they share a common context—royalty or chess. On the other hand, "Queen" (the band) would be placed in a different area of the matrix, alongside other rock bands like "The Beatles" or "Led Zeppelin." This contextual organization allows the LLM to understand relationships between words and retrieve the most relevant information based on the prompt.
The size of the model, such as 175 billion parameters (175B), is like the main library, while smaller models, like 7B, are akin to branch libraries with fewer resources.
Searching the Stacks (The Computational Process):
When you hit "enter" after typing a prompt, it’s like sending your assistant to fetch a book from the third floor. This process is called inference—the time it takes for the LLM to retrieve and generate a response. The model searches for the smallest angle between words, representing their relationships. For example, "dog" and "cat" are closely related (both are pets), while "dog" and "rocketship" are not (the angle between them is large).
Handling Hallucinations:
Sometimes, the librarian might return with a book that doesn’t quite match your request. In the LLM world, this is called a hallucination—when the model generates information that isn’t accurate or relevant. For example, if you ask for information on MrBeast (a famous YouTuber) but get results about lions, tigers, and bears, the model has hallucinated.
Here’s why this happens: the LLM is searching for words that are in the same area of the matrix as "Mr" and/or "Beast." Since it has no specific training on "MrBeast," it looks for the closest related words or angles. In this case, "Beast" might be associated with animals like lions, tigers, and bears, leading the model to generate irrelevant results.
To reduce hallucinations, you can adjust the temperature setting. A lower temperature (e.g., 0.1) makes the model more focused and accurate, while a higher temperature (e.g., 0.9) encourages creativity, which is useful for tasks like writing fiction.
The Power and Limitations of LLMs
The library analogy helps us appreciate the complexity and ingenuity of Large Language Models. Just as a library is a treasure trove of knowledge, an LLM is a powerful tool for understanding and generating human language. However, like any tool, it has its limitations. Hallucinations, biases in training data, and the need for precise prompts remind us that LLMs are not infallible. They are best used as assistants, not oracles.
As AI continues to evolve, so too will the capabilities of LLMs. Whether you’re a researcher, a writer, or simply curious, these models offer a glimpse into the future of human-machine collaboration. So, the next time you interact with an LLM, remember: you’re not just typing into a chatbot—you’re stepping into a grand library of knowledge, guided by a digital librarian.