Large Language Models Explained: How LLMs Work

Quite often, large language models create the illusion of deep, conscious thinking capable of creativity and philosophical discussions. However, behind this intellectual screen hides a purely mathematical algorithm devoid of human consciousness or real understanding of the essence of things. According to its fundamental logic, any LLM works on the principle of a super-powerful text autocompletion system, similar to the T9 function in smartphones. The main and only task of the model at a basic level is the sequential prediction and generation of the most probable next word based on the entire text context that was provided earlier.

At the core of this process lies a strict probabilistic nature, where linguistic constructions turn into statistical patterns. By analyzing gigantic volumes of information, the model calculates the mathematical probability of the appearance of each subsequent element in a sentence. When the system produces an answer, it actually pieces together a sequence of words that, from the perspective of higher mathematics, looks the most natural and expected for a given query.

Quick Take

• The fundamental task of language models is a purely statistical prediction of the next most probable word based on a given context.
• The process of creating text is linear and consists of clear steps: converting words into digital codes, mathematical calculation of options, word selection by filters, and the final output on the screen.
• By combining temperature parameters and selection limits, engineers fine-tune AI for both strict tasks and free copywriting.
• The model sequentially undergoes pre-training, learning the culture of dialogue on exemplary databases, and final training of safe behavior by humans.
• AI is prone to hallucinations because its algorithms are optimized for creating linguistically correct and natural text, rather than for verifying real historical or scientific data.

Text Generation Process

When a user sends a query to a chat, the model launches a complex but very fast process, which in the field of artificial intelligence is called inference. At this stage, modern AI language models turn human words into numbers, calculate millions of options, and step by step assemble the final text.

Step-by-Step Path of a Query

The process of creating each new word consists of a clear sequence of steps that any system based on transformer models undergoes:

1. Prompt. The user enters text, which becomes the starting point and context for calculations.
2. Tokenization. The model cannot read letters directly, so a special algorithm breaks the text into pieces – tokens and converts them into unique digital codes.
3. Prediction. The mathematical heart of the model analyzes the received numbers and calculates the probability for all possible words that can follow.
4. Sampling. The system uses special filters and settings to select one specific word from the list of the most probable candidates.
5. Output. The selected token is converted back into a human word and appears on the screen, after which the entire process repeats to search for the next word.

Word Selection Methods

Exactly what the answer will be – dry and predictable or creative and unexpected – depends on the methods of token selection. Developers of NLP models use different mathematical approaches to guide the imagination of artificial intelligence and filter out nonsensical options.

Here are the main tools that determine the character of a generation:

Understanding these mechanisms is LLM basics. By combining temperature and Top-K/Top-P filters, engineers find the ideal balance for each task. For example, for writing program code, the parameters are set so that the model does not fantasize, but for writing marketing slogans, it is given complete freedom, allowing it to choose less expected but more interesting combinations of words.

Stages of Training

The path of a large language model from initial code to a smart assistant in a chat resembles the process of growing up and getting an education. The neural network undergoes three sequential and resource-intensive stages of training, where each performs its unique role in shaping artificial intelligence.

Pre-training

The first stage is the "omnivorous reading" phase, during which the model absorbs colossal volumes of information. At this stage, the architecture analyzes billions of text documents: digital libraries, scientific articles, encyclopedias, fiction, and open repositories of program code. The main goal of pre-training is to find hidden relationships between words, learn grammar and syntax, and accumulate basic facts about our world.

After completing this stage, the model possesses a gigantic baggage of knowledge, but absolutely does not know how to communicate. If a user asks: "Write a recipe for apple pie", the "raw" neural network, instead of instructions, might simply continue the text in the format of a random article from the Internet: "...my mother told me when we went into the kitchen". It becomes incredibly erudite, but still remains an ordinary text continuation generator that does not understand the concept of dialogue or executing commands.

Fine-tuning and RLHF

Therefore, to turn a chaotic encyclopedia into a useful interlocutor, developers apply methods of purposeful upbringing and adaptation:

1. Supervised Fine-tuning. The model is trained anew, but now on specially created databases in a "question-answer" format. Human trainers manually write ideal lines, showing the system exactly how a good virtual assistant should behave. The neural network learns to understand the structure of commands, recognize user intent, and formulate answers in the form of clear, structured reviews, essays, or lists.
2. Response Rating. As a next step, the system is asked to answer the same question in several different ways. For example, it creates three variants of text, which may differ in tone, detail, or precision of formulations. These variants are passed to experts for evaluation.
3. RLHF. A human evaluates the generated options on a scale of usefulness, politeness, and safety, choosing the best and filtering out the unsuccessful ones. Based on these evaluations, a mathematical reward system is created. It acts as a digital "carrot and stick", punishing the model for toxicity or hallucinations and encouraging it for strict adherence to instructions.

This final stage of socialization guarantees that artificial intelligence will help solve an applied task and refrain from generating harmful or dangerous content.

Limitations and "Illusions" of LLMs

Despite the impressive ability to maintain a conversation, write code, and create texts, large language models remain tools with serious limitations. Understanding the nature of these errors allows for a critical attitude toward AI answers and effective use of its capabilities, avoiding blind trust in the generated content.

Digital Hallucinations

The most famous problem of modern AI is hallucinations – situations where the model, with an absolutely confident look, invents non-existent historical facts, scientific studies, or biographies of people. This happens because the internal architecture of the algorithm is fine-tuned to create plausible, rather than factually accurate text. The model operates with statistical probabilities of word combinations, so if a book title or date invented sounds natural for a given context, the system will present it as the truth without any hesitation. It has no built-in fact-checking mechanism and does not understand the difference between fiction and reality.

Absence of Real Experience

Any language model knows about the world exclusively from the texts it reads during training. It can describe the taste of fresh coffee, the texture of silk, or the sensation of physical pain incredibly poetically, but it has never felt it. This phenomenon in cognitive sciences is called the problem of the absence of real embodiment. Since AI has no physical body, sense organs, or life experience, all its "knowledge" consists only of complex combinations of symbols. Because of this, models often fail before simple logical tasks aimed at understanding the physics of the real world.

Limits of the Context Window

Each model has a strict technical limitation on the volume of information it can hold in its "head" simultaneously during a single conversation – this is called the context window. When a chat session becomes too long, and the number of tokens exceeds this limit, the architecture simply begins to forget the first messages. For the AI, it looks as if the beginning of your conversation never existed.

Although developers are constantly increasing the sizes of context windows, this limitation remains an architectural challenge, since processing gigantic volumes of text in real time requires an exponential growth in the computing power of servers.

FAQ

What are the "parameters" of a language model, and what does their number affect?

Parameters are the internal numerical coefficients of a neural network, which can be compared to synapses in the human brain. During training, the model adjusts these numbers to capture complex patterns and connections between words. The more parameters a model has, the finer nuances of language it is capable of catching, and the more complex logical tasks it can solve.

Why do models work precisely with tokens, rather than with whole words or individual letters?

Letter-by-letter analysis would force the model to spend too many computing resources on basic word assembly, while working with whole words would expand the AI's vocabulary to astronomical scales due to different cases and word forms. Tokenization is the ideal compromise: it breaks text into common syllables and pieces of words. This allows the model to effectively save memory and easily understand even new or invented words by analyzing their familiar parts.

Why do LLMs consume so much electricity and require special graphics cards?

The process of calculating probabilities for each subsequent word requires the simultaneous execution of billions of mathematical operations with matrices. Conventional central processing units process tasks sequentially, making them too slow for AI. Graphics cards and specialized tensor chips are designed for massive parallel computations; however, the operation of thousands of such GPUs in data centers creates a huge load on power grids and requires complex cooling systems.

What are the emergent properties of large language models?

These are unique abilities of AI that suddenly appear in models only when a certain critical scale of parameters and volume of training data is reached. Small neural networks can only chaotically continue phrases, but when a model is scaled hundreds of times, it unexpectedly acquires skills of step-by-step logical thinking, translation of rare languages, or writing program code. These properties are not programmed manually by engineers but arise as a side effect of the system's complexity.

How do developers fight the problem of hallucinations in commercial AI systems?

The main method of combat is RAG technology, which connects the LLM to external verified databases or search engines. When a user asks a question, the system first finds factual documents on the topic, attaches them to the query, and asks the AI to formulate an answer exclusively based on the provided facts. This reduces the risk of inventing information to a minimum and provides answers with up-to-date links to sources.

What is the difference between an open model and a closed one?

Closed models (such as ChatGPT or Claude) are accessible only through the cloud interfaces of the developer companies; their source code and architectural weights are strictly classified. Open models (such as Llama from Meta or Mistral) allow any developer to download the entire array of parameters to their own server for free. This gives businesses full control over data privacy and the ability to deeply customize AI for specific industrial tasks.

What is multimodality and how does it change classic language models?

Multimodality is the ability of a model to work with different types of input data simultaneously, going beyond a purely text format. Thanks to a shared vector architecture, modern AI can accept text, images, audio, and video within a single query. This allows the model, for example, to look at a photograph of a broken mechanism and write a step-by-step instruction for its repair in text, combining visual and linguistic perception.